TW201928874A - Method for improving quality of enlarged image - Google Patents
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Abstract
Description
本揭露實施例是有關於一種改善放大圖像品質的方法，且特別是有關於一種改善放大圖像之邊緣清晰度的方法。 The disclosed embodiments are directed to a method of improving the quality of an enlarged image, and more particularly to a method of improving the sharpness of an edge of an enlarged image.
為了把一個尺寸較小的圖像放大到尺寸較大的圖像，可以首先依據原圖像的像素點的位置，按照放大倍數找到新的像素點的位置，並依據原圖像的多個相鄰的像素點的色彩值，來對新的像素點的色彩值進行估算並填充，即像素插值(Interpolation)。對於上述的像素插值存在很多不同的演算法，其中最常用的是最鄰近點插值演算法和雙線性插值演算法。最鄰近點插值演算法是最簡單且速度最快的演算法，但生成的放大圖像會呈現馬賽克和鋸齒等明顯失真現象。雙線性插值演算法增加了像素點計算數量，圖像視覺效果有明顯的提升，但會有一定的圖像邊緣模糊或字體模糊的現象。為了得到更好的圖像放大品質，也有許多新的演算法被不斷提出，這些新的演算法大多通過擴大參照像素點的 範圍和進行更複雜的插值計算方式來提高像素插值的逼真程度，但都無法克服放大圖像邊緣模糊的固有缺陷。 In order to enlarge a small image to a larger image, the position of the pixel may be found according to the position of the pixel of the original image according to the magnification, and according to the multiple phases of the original image. The color value of the adjacent pixel is used to estimate and fill the color value of the new pixel, that is, pixel interpolation. There are many different algorithms for pixel interpolation as described above, the most common ones being the nearest neighbor interpolation algorithm and the bilinear interpolation algorithm. The nearest neighbor interpolation algorithm is the simplest and fastest algorithm, but the resulting magnified image will exhibit significant distortion such as mosaic and aliasing. The bilinear interpolation algorithm increases the number of pixel calculations, and the visual effect of the image is obviously improved, but there will be a certain image edge blur or font blur. In order to get better image quality, there are also many new algorithms that are constantly being proposed. Most of these new algorithms are extended by reference pixels. Range and more complex interpolation calculations to improve the fidelity of pixel interpolation, but can not overcome the inherent defects of magnified image edge blur.
本揭露之目的在於提出一種改善放大圖像品質的方法，透過改善放大圖像之邊緣的清晰度來克服傳統圖像放大方法之放大圖像邊緣模糊的缺陷。更進一步，改善放大圖像之邊緣的平滑度以改善放大圖像邊緣鋸齒的現象。 The purpose of the present disclosure is to provide a method for improving the quality of an enlarged image, which overcomes the defect of the edge blurring of the enlarged image by the conventional image enlargement method by improving the sharpness of the edge of the enlarged image. Further, the smoothness of the edges of the enlarged image is improved to improve the phenomenon of aliasing of the edges of the enlarged image.
根據本揭露之上述目的，提出一種改善放大圖像品質的方法，用以改善從原圖像放大為放大圖像之邊緣清晰度，其中原圖像包含多個原像素且放大圖像包含多個新像素，改善放大圖像品質的方法包含以下步驟：對原圖像中之目標像素區域進行圖像邊緣偵測，用以判斷目標像素區域是否包含圖像邊緣；以及當目標像素區域包含圖像邊緣時，針對放大圖像中相應於目標像素區域以及與目標像素區域部分重疊的三個相鄰像素區域的多個新像素進行圖像清晰調整，其中目標像素區域與相鄰像素區域大小相同，且目標像素區域與相鄰像素區域分別包含有相同之多個原像素之一者。 According to the above object of the present disclosure, a method for improving the quality of an enlarged image is proposed to improve the edge definition from the original image to the enlarged image, wherein the original image includes a plurality of original pixels and the enlarged image includes a plurality of The new pixel, the method for improving the quality of the enlarged image includes the following steps: performing image edge detection on the target pixel area in the original image to determine whether the target pixel area includes an image edge; and when the target pixel area contains an image At the edge, the image is sharply adjusted for a plurality of new pixels in the enlarged image corresponding to the target pixel region and three adjacent pixel regions partially overlapping the target pixel region, wherein the target pixel region is the same size as the adjacent pixel region. And the target pixel area and the adjacent pixel area respectively include one of the same plurality of original pixels.
在一些實施例中，上述圖像邊緣偵測係利用目標像素區域與一組樣式像素區域進行比對，以判斷目標像素區域是否包含圖像邊緣。 In some embodiments, the image edge detection uses the target pixel area to compare with a set of pattern pixel areas to determine whether the target pixel area includes an image edge.
在一些實施例中，當目標像素區域不包含圖像邊緣時，透過像素插值法計算得知放大圖像中相應於目標像 素區域的新像素的灰階值。 In some embodiments, when the target pixel region does not include an image edge, the pixel image is calculated to be corresponding to the target image in the enlarged image. The grayscale value of the new pixel of the prime region.
在一些實施例中，當目標像素區域包含圖像邊緣時，利用目標像素區域以及相鄰像素區域的組合來與一組樣式微區域進行比對，以決定出相應樣式微區域，從而根據相應樣式微區域所對應的灰階值表來得知放大圖像中相應於目標像素區域的新像素的灰階值。 In some embodiments, when the target pixel region includes an image edge, the target pixel region and the combination of adjacent pixel regions are used to compare with a set of pattern micro regions to determine a corresponding pattern micro region, thereby according to the corresponding pattern. The gray scale value table corresponding to the micro area is used to know the gray scale value of the new pixel corresponding to the target pixel area in the enlarged image.
在一些實施例中，上述圖像清晰調整包含以下步驟：判斷目標像素區域以及相鄰像素區域是否存在相互影響；以及當目標像素區域以及相鄰像素區域存在相互影響時，對放大圖像中相應於目標像素區域以及相鄰像素區域的新像素的灰階值進行調整。 In some embodiments, the image clarity adjustment includes the following steps: determining whether the target pixel region and the adjacent pixel region have mutual influence; and corresponding to the enlarged image when the target pixel region and the adjacent pixel region have mutual influence The grayscale values of the new pixels in the target pixel area and the adjacent pixel area are adjusted.
在一些實施例中，上述改善放大圖像品質的方法更包含：當放大圖像中的目標微區域包含有傾斜邊緣時，針對目標微區域所包含的新像素進行圖像邊緣平滑調整以改善放大圖像之邊緣平滑度。 In some embodiments, the method for improving the quality of the enlarged image further includes: performing image edge smoothing adjustment on the new pixel included in the target micro region to improve the magnification when the target micro region in the enlarged image includes the oblique edge. The edge smoothness of the image.
在一些實施例中，上述圖像邊緣平滑調整係保持傾斜邊緣的方向，並改變相應於傾斜邊緣的新像素所相鄰的新像素之至少一者的灰階值。 In some embodiments, the image edge smoothing adjustment maintains the direction of the oblique edge and changes the grayscale value of at least one of the new pixels adjacent to the new pixel corresponding to the oblique edge.
在一些實施例中，上述圖像邊緣平滑調整係保持傾斜邊緣的方向，並改變相應於傾斜邊緣的新像素所相鄰的新像素之至少一者的灰階值，且改變相應於傾斜邊緣的新像素之至少一者的灰階值。 In some embodiments, the image edge smoothing adjustment maintains the direction of the oblique edge and changes the grayscale value of at least one of the new pixels adjacent to the new pixel corresponding to the oblique edge, and changes the corresponding to the oblique edge. The grayscale value of at least one of the new pixels.
在一些實施例中，上述像素插值法為最鄰近點插值算法或雙線性差值算法。 In some embodiments, the pixel interpolation method described above is a nearest neighbor interpolation algorithm or a bilinear difference algorithm.
為讓本揭露的上述特徵和優點能更明顯易懂，下文特舉實施例，並配合所附圖式作詳細說明如下。 The above described features and advantages of the present invention will be more apparent from the following description.
S1-S4、S11、S12、S21-S24‧‧‧步驟 S1-S4, S11, S12, S21-S24‧‧‧ steps
100‧‧‧原圖像 100‧‧‧ original image
10011-10044‧‧‧原像素 100 11 -100 44 ‧‧‧ original pixels
20011-20022、30011-30044、40011-40033‧‧‧新像素 200 11 -200 22 , 300 11 -300 44 , 400 11 -400 33 ‧‧‧ New pixels
110‧‧‧目標像素區域 110‧‧‧Target pixel area
120、130、140‧‧‧相鄰像素區域 120, 130, 140‧‧‧ adjacent pixel areas
R‧‧‧紅色子像素 R‧‧‧Red subpixel
G‧‧‧綠色子像素 G‧‧‧Green subpixel
B‧‧‧藍色子像素 B‧‧‧Blue subpixel
從以下結合所附圖式所做的詳細描述，可對本揭露之態樣有更佳的了解。需注意的是，根據業界的標準實務，各特徵並未依比例繪示。事實上，為了使討論更為清楚，各特徵的尺寸都可任意地增加或減少。 A better understanding of the aspects of the present disclosure can be obtained from the following detailed description taken in conjunction with the drawings. It should be noted that, according to industry standard practices, the features are not drawn to scale. In fact, in order to make the discussion clearer, the dimensions of each feature can be arbitrarily increased or decreased.
[圖1]係繪示根據本揭露的第一實施例之原像素與新像素的分布示意圖。 FIG. 1 is a schematic diagram showing the distribution of an original pixel and a new pixel according to a first embodiment of the present disclosure.
[圖2]係繪示根據本揭露的實施例之改善放大圖像品質的方法之流程圖。 FIG. 2 is a flow chart showing a method for improving the quality of an enlarged image according to an embodiment of the present disclosure.
[圖3]係繪示根據本揭露的第一實施例之一幅包含多個原像素的原圖像的示意圖。 FIG. 3 is a schematic diagram showing an original image including a plurality of original pixels according to a first embodiment of the present disclosure.
[圖4]係繪示根據本揭露的實施例之改善放大圖像品質的方法之步驟的細部流程圖。 FIG. 4 is a detailed flow chart showing the steps of a method for improving the quality of an enlarged image according to an embodiment of the present disclosure.
[圖5]係繪示根據本揭露的第一實施例之一組樣式像素區域的示意圖。 FIG. 5 is a schematic diagram showing a group pattern pixel area according to a first embodiment of the present disclosure.
[圖6]係繪示根據本揭露的第二實施例之一組樣式像素區域的示意圖。 FIG. 6 is a schematic diagram showing a group pattern pixel area according to a second embodiment of the present disclosure.
[圖7]係繪示根據本揭露的第一實施例之一幅包含多個原像素的原圖像的示意圖。 FIG. 7 is a schematic diagram showing an original image including a plurality of original pixels according to a first embodiment of the present disclosure.
[圖8a]至[圖8c]係繪示根據本揭露的第一實施例之圖像清晰調整的示意圖。 [Fig. 8a] to [Fig. 8c] are schematic diagrams showing image sharpness adjustment according to the first embodiment of the present disclosure.
[圖9a]至[圖9b]係繪示根據本揭露的第三實施例之一幅包含3×3個原像素的原圖像與一幅包含5×5個新像素的放大圖像的示意圖。 [Fig. 9a] to [Fig. 9b] are schematic views showing an original image including 3 × 3 original pixels and an enlarged image containing 5 × 5 new pixels according to a third embodiment of the present disclosure. .
[圖10a]至[圖10b]與[圖11a]至[圖11b]係繪示根據本揭露的第四實施例之圖像邊緣平滑調整的示意圖。 10a to 10b and FIG. 11a to FIG. 11b are schematic diagrams showing image edge smoothing adjustment according to a fourth embodiment of the present disclosure.
[圖10a]和[圖10c]與[圖11a]和[圖11c]係繪示根據本揭露的第五實施例之圖像邊緣平滑調整的示意圖。 [Fig. 10a] and [Fig. 10c] and [Fig. 11a] and [Fig. 11c] are schematic diagrams showing image edge smoothing adjustment according to the fifth embodiment of the present disclosure.
[圖12a]至[圖12b]係繪示根據本揭露的第六實施例之圖像邊緣平滑調整的示意圖。 [Fig. 12a] to [Fig. 12b] are schematic diagrams showing image edge smoothing adjustment according to a sixth embodiment of the present disclosure.
[圖12a]和[圖12c]係繪示根據本揭露的第七實施例之圖像邊緣平滑調整的示意圖。 [Fig. 12a] and [Fig. 12c] are schematic diagrams showing image edge smoothing adjustment according to a seventh embodiment of the present disclosure.
[圖13a]至[圖13b]係繪示根據本揭露的第八實施例之圖像邊緣平滑調整的示意圖。 [Fig. 13a] to [Fig. 13b] are schematic diagrams showing image edge smoothing adjustment according to the eighth embodiment of the present disclosure.
[圖13a]和[圖13c]係繪示根據本揭露的第九實施例之圖像邊緣平滑調整的示意圖。 [Fig. 13a] and [Fig. 13c] are schematic diagrams showing image edge smoothing adjustment according to a ninth embodiment of the present disclosure.
[圖14a]至[圖14b]與[圖15a]至[圖15b]係繪示根據本揭露的第十實施例之圖像邊緣平滑調整的示意圖。 [Fig. 14a] to [Fig. 14b] and [Fig. 15a] to [Fig. 15b] are schematic diagrams showing image edge smoothing adjustment according to the tenth embodiment of the present disclosure.
以下仔細討論本發明的實施例。然而，可以理解的是，實施例提供許多可應用的概念，其可實施於各式各樣的特定內容中。所討論、揭示之實施例僅供說明，並非用以限定本發明之範圍。 Embodiments of the invention are discussed in detail below. However, it will be appreciated that the embodiments provide many applicable concepts that can be implemented in a wide variety of specific content. The examples discussed and disclosed are illustrative only and are not intended to limit the scope of the invention.
在本揭露的實施例中，包含多個原像素的原圖 像被放大為包含多個新像素的放大圖像。舉例來說，假設有一幅包含3×3個原像素的原圖像，將其放大4/3倍，即成為一幅包含4×4個新像素的放大圖像。一種較為容易理解的圖像放大概念是：在原圖像上放置虛構的4×4個柵格；然後，採用某種方法將每一柵格全部賦值；最後，將這些柵格擴展到放大圖像的大小。如此，完成了將原圖像放大為放大圖像的放大操作。 In the embodiment of the present disclosure, an original image including a plurality of original pixels The image is magnified to a magnified image containing multiple new pixels. For example, suppose an original image containing 3 × 3 original pixels is enlarged by 4/3 times to become a magnified image containing 4 × 4 new pixels. An easy-to-understand image magnification concept is to place fictitious 4×4 grids on the original image; then, assign each grid all values in some way; finally, extend the grid to a magnified image. the size of. In this way, the enlargement operation of enlarging the original image into the enlarged image is completed.
原像素與新像素之位置的關係可以利用圖1來幫助理解。圖1係繪示根據本揭露的第一實施例之原像素與新像素的分布示意圖。在圖1中，符號○表示原像素，符號+表示新像素，符號⊕表示重疊的像素，即位在同一位置的原像素和新像素。圖1係例示一幅包含3×3個原像素的原圖像將放大4/3倍後成為一幅包含4×4個新像素的放大圖像。 The relationship between the original pixel and the location of the new pixel can be aided by Figure 1. FIG. 1 is a schematic diagram showing the distribution of an original pixel and a new pixel according to a first embodiment of the present disclosure. In FIG. 1, the symbol ○ represents the original pixel, the symbol + represents the new pixel, and the symbol ⊕ represents the overlapping pixels, that is, the original pixel and the new pixel located at the same position. Figure 1 illustrates that an original image containing 3 x 3 original pixels will be magnified 4/3 times to become a magnified image containing 4 x 4 new pixels.
本揭露提出一種改善放大圖像品質的方法，請參照圖2，圖2係繪示根據本揭露的實施例之改善放大圖像品質的方法之流程圖。於步驟S1，在原圖像中取一個包含N×M個原像素的區域，並將此區域定義為欲處理的目標像素區域，並對目標像素區域進行圖像邊緣偵測，用以判斷目標像素區域是否包含圖像邊緣，其中N、M為正整數，且N與M之至少一者為大於2的正整數。 The present disclosure provides a method for improving the quality of an enlarged image. Referring to FIG. 2, FIG. 2 is a flowchart of a method for improving the quality of an enlarged image according to an embodiment of the present disclosure. In step S1, an area including N×M original pixels is taken in the original image, and the area is defined as a target pixel area to be processed, and image edge detection is performed on the target pixel area to determine the target pixel. Whether the region contains an image edge, where N, M are positive integers, and at least one of N and M is a positive integer greater than two.
以下以圖3來對步驟S1做例示性的說明，圖3係繪示根據本揭露的第一實施例之一幅包含多個原像素的原圖像100的示意圖。原圖像100包含原像素10011、10012、10013、10014、10021、10022、10023、10024、10031、 10032、10033、10034、10041、10042、10043、10044...等等。在原圖像100中取一個包含2×2個原像素的目標像素區域110，換言之，N=M=2，目標像素區域110包含原像素10011、10012、10021、10022。 Step S1 is exemplarily described below with reference to FIG. 3. FIG. 3 is a schematic diagram showing an original image 100 including a plurality of original pixels according to the first embodiment of the present disclosure. The original image 100 includes the original pixels 100 11 , 100 12 , 100 13 , 100 14 , 100 21 , 100 22 , 100 23 , 100 24 , 100 31 , 100 32 , 100 33 , 100 34 , 100 41 , 100 42 , 100 43 , 100 44 ... and so on. A target pixel region 110 including 2 × 2 original pixels is taken in the original image 100, in other words, N = M = 2, and the target pixel region 110 includes the original pixels 100 11 , 100 12 , 100 21 , 100 22 .
請回到圖2，於步驟S2，根據目標像素區域包含圖像邊緣與否，來計算放大圖像中相應於目標像素區域的多個新像素的灰階值。請參照圖4，圖4係繪示根據本揭露的實施例之改善放大圖像品質的方法之步驟S1-S2的細部流程圖。於步驟S11，利用目標像素區域與一組樣式像素區域進行比對。於步驟S12，藉由比對結果來得知目標像素區域是否包含圖像邊緣。其中，目標像素區域與樣式像素區域大小相同，換言之，樣式像素區域為一組包含N×M個像素的組合。再者，樣式像素區域為一組包含所有圖像邊緣樣式的組合。 Returning to FIG. 2, in step S2, the grayscale values of the plurality of new pixels corresponding to the target pixel region in the enlarged image are calculated according to whether the target pixel region includes the image edge or not. Please refer to FIG. 4. FIG. 4 is a detailed flowchart of steps S1-S2 of the method for improving the quality of an enlarged image according to an embodiment of the present disclosure. In step S11, the target pixel area is compared with a set of pattern pixel areas. In step S12, it is known by the comparison result whether the target pixel area contains an image edge. The target pixel area is the same size as the pattern pixel area, in other words, the pattern pixel area is a set of combinations comprising N×M pixels. Furthermore, the style pixel area is a set of combinations containing all image edge styles.
以圖5來對樣式像素區域做例示性的說明，圖5係繪示根據本揭露的第一實施例之一組樣式像素區域的示意圖。如圖5所示，樣式像素區域為一組包含2×2個像素的16種實現情況的組合，換言之，N=M=2。此外，以圖6來對樣式像素區域做例示性的說明，圖6係繪示根據本揭露的第二實施例之一組樣式像素區域的示意圖。如圖6所示，樣式像素區域為一組包含3×3個像素的19種實現情況的組合，換言之，N=M=3。 An exemplary description of a pattern pixel area is shown in FIG. 5. FIG. 5 is a schematic diagram showing a group pattern pixel area according to the first embodiment of the present disclosure. As shown in FIG. 5, the pattern pixel area is a combination of 16 implementations including 2 x 2 pixels, in other words, N=M=2. In addition, the pattern pixel area is exemplarily illustrated in FIG. 6, and FIG. 6 is a schematic diagram showing a group pattern pixel area according to the second embodiment of the present disclosure. As shown in FIG. 6, the pattern pixel area is a combination of 19 implementations including 3×3 pixels, in other words, N=M=3.
請回到圖4，當目標像素區域不包含圖像邊緣，則進行步驟S21，透過像素插值法計算得知放大圖像中相應 於目標像素區域的新像素的灰階值(步驟S24)。具體而言，當目標像素區域不包含圖像邊緣，則不會產生邊緣模糊的現象，因此可採用習知的像素插值法來計算放大圖像中相應於目標像素區域的新像素的灰階值。其中，習知的像素插值法可為例如最鄰近點插值算法、雙線性差值算法等。值得一提的是，本揭露並不限定像素插值法的演算方式，使用者可視實際需求選擇適合的習知像素插值法。 Please return to FIG. 4, when the target pixel area does not include an image edge, proceed to step S21, and calculate the corresponding image in the enlarged image by pixel interpolation method. The grayscale value of the new pixel in the target pixel area (step S24). Specifically, when the target pixel region does not include an image edge, the edge blur phenomenon does not occur, so a conventional pixel interpolation method can be used to calculate the grayscale value of the new pixel corresponding to the target pixel region in the enlarged image. . The conventional pixel interpolation method may be, for example, a nearest neighbor interpolation algorithm, a bilinear difference algorithm, or the like. It is worth mentioning that the disclosure does not limit the calculation method of the pixel interpolation method, and the user can select a suitable conventional pixel interpolation method according to actual needs.
請再次參照圖4，當目標像素區域包含圖像邊緣，則進行步驟S22，利用目標像素區域以及與目標像素區域部分重疊的三個相鄰像素區域的組合來與一組樣式微區域進行比對，以決定出相應樣式微區域。接著，進行步驟S23，根據相應樣式微區域所對應的灰階值表來得知放大圖像中相應於目標像素區域的新像素的灰階值。 Referring to FIG. 4 again, when the target pixel region includes the image edge, proceeding to step S22, the target pixel region and the combination of three adjacent pixel regions partially overlapping the target pixel region are used to compare with a set of pattern micro regions. To determine the corresponding style micro area. Next, in step S23, the grayscale value of the new pixel corresponding to the target pixel region in the enlarged image is obtained according to the grayscale value table corresponding to the corresponding pattern microregion.
以圖7來對相鄰像素區域做例示性的說明，圖7係繪示根據本揭露的第一實施例之一幅包含多個原像素的原圖像100的示意圖。請一併參照圖3與圖7，相鄰像素區域120、130、140與目標像素區域110大小相同，換言之，相鄰像素區域120、130、140分別包含2×2個原像素。相鄰像素區域120包含原像素10021、10022、10031、10032，相鄰像素區域130包含原像素10012、10013、10022、10023，相鄰像素區域140包含原像素10022、10023、10032、10033。具體而言，目標像素區域110與相鄰像素區域120、130、140部分重疊且分別包含有相同之多個原像素之一者(即原像素10022)。值得一提的是，目標像素區域與三個相鄰像素 區域的組合為包含(2N-1)×(2M-1)個像素的區域。 An exemplary embodiment of an adjacent pixel region is illustrated in FIG. 7. FIG. 7 is a schematic diagram showing an original image 100 including a plurality of original pixels according to a first embodiment of the present disclosure. Referring to FIG. 3 and FIG. 7 together, the adjacent pixel regions 120, 130, and 140 are the same size as the target pixel region 110. In other words, the adjacent pixel regions 120, 130, and 140 respectively include 2 × 2 original pixels. The adjacent pixel area 120 includes the original pixels 100 21 , 100 22 , 100 31 , 100 32 , the adjacent pixel area 130 includes the original pixels 100 12 , 100 13 , 100 22 , 100 23 , and the adjacent pixel area 140 includes the original pixels 100 22 . , 100 23 , 100 32 , 100 33 . Specifically, the target pixel region 110 partially overlaps the adjacent pixel regions 120, 130, 140 and respectively includes one of the same plurality of original pixels (ie, the original pixel 100 22 ). It is worth mentioning that the combination of the target pixel area and the three adjacent pixel areas is an area containing (2N-1)×(2M-1) pixels.
在本揭露中，預先定義的一組樣式微區域，並利用目標像素區域以及三個相鄰像素區域的組合與其比對，並以樣式微區域中比對結果吻合者作為相應樣式微區域。其中，目標像素區域以及三個相鄰像素區域的組合與樣式微區域大小相同，換言之，樣式微區域為一組包含(2N-1)×(2M-1)個像素的組合。此外，透過預測試來建立樣式微區域所分別對應的灰階值表。如此一來，即可根據相應樣式微區域所對應的灰階值表來得知放大圖像中相應於目標像素區域的新像素的灰階值。 In the present disclosure, a predefined set of pattern micro-areas is used, and the target pixel area and a combination of three adjacent pixel areas are used to compare with each other, and the matching result in the pattern micro-area is used as the corresponding pattern micro-area. The combination of the target pixel region and the three adjacent pixel regions is the same as the size of the pattern micro region, in other words, the pattern micro region is a group comprising a combination of (2N-1)×(2M−1) pixels. In addition, a pre-test is used to establish a grayscale value table corresponding to the style micro-regions. In this way, the grayscale value of the new pixel corresponding to the target pixel region in the enlarged image can be known according to the grayscale value table corresponding to the corresponding pattern microregion.
請回到圖2，在步驟S2之後，判斷目標像素區域以及相鄰像素區域是否存在相互影響，當目標像素區域以及相鄰像素區域存在相互影響時，進行步驟S3，即進行圖像清晰調整，對放大圖像中相應於目標像素區域以及相鄰像素區域的新像素的灰階值進行調整。 Returning to FIG. 2, after step S2, it is determined whether the target pixel area and the adjacent pixel area have mutual influence. When the target pixel area and the adjacent pixel area have mutual influence, step S3 is performed, that is, image clear adjustment is performed. The grayscale value of the new pixel corresponding to the target pixel area and the adjacent pixel area in the enlarged image is adjusted.
以圖8a至圖8c來對圖像清晰調整做例示性的說明，圖8a至圖8c係繪示根據本揭露的第一實施例之圖像清晰調整的示意圖。圖8a為包含3×3個原像素的原圖像，假設放大倍率為4/3，經過本揭露之改善放大圖像品質的方法之步驟S1-S2後，所得者為圖8b，即包含4×4個新像素的放大圖像。比較圖8a與圖8b可知，放大圖像(圖8b)呈現出原圖像(圖8a)之傾斜邊緣趨勢中所沒有的點(以灰色表示者)，如此一來將會造成觀看放大圖像(圖8b)時，有邊緣模糊的現象產生。具體而言，由於原圖像(圖8a)中的目標像素 區域與相鄰像素區域存在相互影響，使得放大圖像(圖8b)時有邊緣模糊的現象產生。圖8c為經過本揭露之的實施例之改善放大圖像品質的方法之步驟S3(即圖像清晰調整)後的放大圖像。具體而言，經過圖像清晰調整後，消除了放大圖像(圖8b)中相應於原圖像(圖8a)邊緣趨勢中所沒有的點，因此放大圖像(圖8c)具有清晰的邊緣顯示效果。 An exemplary description of the image sharp adjustment is made with reference to FIG. 8a to FIG. 8c, and FIGS. 8a to 8c are schematic diagrams showing image sharp adjustment according to the first embodiment of the present disclosure. 8a is an original image including 3 × 3 original pixels, assuming a magnification of 4/3, after the steps S1-S2 of the method for improving the image quality of the present disclosure, the result is FIG. 8b, which includes 4 × 4 enlarged images of new pixels. Comparing Fig. 8a with Fig. 8b, the magnified image (Fig. 8b) presents a point (in gray) that is not in the oblique edge trend of the original image (Fig. 8a), which will result in viewing the enlarged image. (Fig. 8b), there is a phenomenon of edge blurring. Specifically, due to the target pixel in the original image (Fig. 8a) The area interacts with adjacent pixel areas, causing edge blurring when magnifying the image (Fig. 8b). FIG. 8c is an enlarged image of step S3 (ie, image sharp adjustment) of the method for improving the image quality of the embodiment of the present disclosure. Specifically, after the image is clearly adjusted, the points in the enlarged image (Fig. 8b) corresponding to the edge trends of the original image (Fig. 8a) are eliminated, so that the enlarged image (Fig. 8c) has sharp edges. display effect.
圖9a至圖9b係繪示根據本揭露的第三實施例之一幅包含3×3個原像素的原圖像與一幅包含5×5個新像素的放大圖像的示意圖。圖9a為包含3×3個原像素的原圖像，假設放大倍率為5/3，經過本揭露之的實施例之改善放大圖像品質的方法之步驟S1-S3後，所得者為圖9b，即包含5×5個新像素的放大圖像。具體而言，經過圖像清晰調整後，放大圖像(圖9b)具有清晰的邊緣顯示效果。 9a-9b are schematic diagrams showing an original image including 3×3 original pixels and an enlarged image including 5×5 new pixels according to a third embodiment of the present disclosure. FIG. 9a is an original image including 3×3 original pixels, assuming a magnification of 5/3, and after the steps S1-S3 of the method for improving the image quality of the embodiment of the present disclosure, the result is FIG. 9b. That is, a magnified image containing 5 × 5 new pixels. Specifically, after the image is clearly adjusted, the magnified image (Fig. 9b) has a clear edge display effect.
值得一提的是，在本揭露的實施例中，圖像清晰調整的原則為，原圖像中若包含一定像素寬度之邊緣(例如直線或曲線)，其線寬乘上放大倍率後之數值若不為整數，則僅取整數。舉例來說，若原圖像中包含像素寬度為1的一段直線，且放大倍率為1.5倍，則放大圖像中對應該直線的直線的像素寬度仍為1。 It is worth mentioning that, in the embodiment of the present disclosure, the principle of image sharp adjustment is that if the original image includes an edge of a certain pixel width (for example, a straight line or a curve), the line width is multiplied by the magnification value. If it is not an integer, only an integer is taken. For example, if the original image includes a straight line with a pixel width of 1 and the magnification is 1.5 times, the pixel width of the line corresponding to the straight line in the enlarged image is still 1.
應注意的是，當某一目標像素區域完成步驟S1-S3之後，則繼續對下一個包含N×M個原像素的目標像素區域執行步驟S1-S3，直到原圖像中的所有原像素皆包含於目標像素區域以及與目標像素區域部分重疊的三個相鄰像素區域的組合中。舉例來說，請參照圖3，當目標像素區 域110完成步驟S1-S3之後，則繼續對下一個包含2×2個原像素(即原像素10013、10014、10023、10024)的目標像素區域執行步驟S1-S3。舉例來說，請參照圖3，當原圖像中的所有位於第一列與第二列的原像素皆包含於目標像素區域以及與目標像素區域部分重疊的三個相鄰像素區域的組合中之後，則繼續對下一個包含2×2個原像素(即原像素10031、10032、10041、10042)的目標像素區域執行步驟S1-S3。依此類推。 It should be noted that after a certain target pixel region completes steps S1-S3, steps S1-S3 are continued to be performed on the next target pixel region including N×M original pixels until all the original pixels in the original image are It is included in a combination of a target pixel area and three adjacent pixel areas partially overlapping the target pixel area. For example, referring to FIG. 3, after the target pixel region 110 completes steps S1-S3, it continues to the next one containing 2×2 original pixels (ie, the original pixels 100 13 , 100 14 , 100 23 , 100 24 ). The target pixel area performs steps S1-S3. For example, referring to FIG. 3, when all the original pixels in the first column and the second column in the original image are included in the combination of the target pixel region and the three adjacent pixel regions partially overlapping the target pixel region, Thereafter, steps S1 - S3 are continued to the next target pixel region including 2 × 2 original pixels (ie, the original pixels 100 31 , 100 32 , 100 41 , 100 42 ). So on and so forth.
請回到圖2，在步驟S3之後，判斷放大圖像中的目標微區域是否包含傾斜邊緣，當放大圖像中的目標微區域包含傾斜邊緣時，進行步驟S4，針對目標微區域所包含的新像素進行圖像邊緣平滑調整以改善放大圖像之邊緣平滑度。其中，圖像邊緣平滑調整係保持傾斜邊緣的方向，並改變相應於傾斜邊緣的新像素所相鄰的新像素之至少一者的灰階值。或者，圖像邊緣平滑調整係保持傾斜邊緣的方向，並改變相應於傾斜邊緣的新像素所相鄰的新像素之至少一者的灰階值，且改變相應於傾斜邊緣的新像素之至少一者的灰階值。其中，目標微區域可為使用者自行定義的包含多個新像素且欲處理的區域，換言之，目標微區域可為放大圖像中的一個或多個區域，因此，對本揭露的實施例而言，將依序針對所有的目標微區域執行步驟S4。 Returning to FIG. 2, after step S3, it is determined whether the target micro-area in the enlarged image includes a tilted edge, and when the target micro-area in the enlarged image includes the inclined edge, proceeding to step S4 for the target micro-area included The new pixel performs image edge smoothing adjustment to improve the edge smoothness of the enlarged image. Wherein, the image edge smoothing adjustment maintains the direction of the oblique edge and changes the grayscale value of at least one of the new pixels adjacent to the new pixel corresponding to the oblique edge. Alternatively, the image edge smoothing adjustment maintains the direction of the oblique edge and changes the grayscale value of at least one of the new pixels adjacent to the new pixel corresponding to the oblique edge, and changes at least one of the new pixels corresponding to the oblique edge The grayscale value of the person. The target micro-area may be a user-defined area containing a plurality of new pixels and to be processed, in other words, the target micro-area may be one or more areas in the enlarged image, and thus, for the embodiment of the present disclosure Step S4 will be performed sequentially for all target micro-regions.
以圖10a至圖10c來對圖像邊緣平滑調整做例示性的說明，圖10a至圖10c係繪示根據本揭露的第四實施例與第五實施例之圖像邊緣平滑調整的示意圖。圖10a呈現 放大圖像中包含傾斜邊緣的目標微區域。這樣的傾斜邊緣將會造成觀看時，有邊緣鋸齒的現象產生。圖10b為根據本揭露的第四實施例之圖像邊緣平滑調整後的示意圖。圖10b呈現出圖像邊緣平滑調整係保持圖10a之傾斜邊緣的方向，並調整相應於傾斜邊緣的新像素所相鄰的兩個新像素20012、20021的子像素的灰階值。對圖10b而言，改變了新像素20012的紅色子像素R的灰階值，其中新像素20012的紅色子像素R的灰階值等於新像素20011的紅色子像素的灰階值、新像素20022的紅色子像素的灰階值或兩者之平均。再者，改變了新像素20021的藍色子像素B的灰階值，其中新像素20021的藍色子像素B的灰階值等於新像素20022的藍色子像素的灰階值、新像素20011的藍色子像素的灰階值或兩者之平均。此外，圖10c為根據本揭露的第五實施例之圖像邊緣平滑調整後的示意圖。圖10c與圖10b之不同之處為，圖10c更改變了新像素20012的綠色子像素G的灰階值，其中新像素20012的綠色子像素G的灰階值等於新像素20011的綠色子像素的灰階值、新像素20022的綠色子像素的灰階值或兩者之平均。再者，圖10c更改變了新像素20021的綠色子像素G的灰階值，其中新像素20021的綠色子像素G的灰階值等於新像素20022的綠色子像素的灰階值、新像素20011的綠色子像素的灰階值或兩者之平均。具體而言，經過圖像邊緣平滑調整後，使得原本邊緣鋸齒的現象得以平滑。 An exemplary description of image edge smoothing adjustment is made in FIGS. 10a to 10c, and FIGS. 10a to 10c are schematic diagrams showing image edge smoothing adjustment according to the fourth embodiment and the fifth embodiment of the present disclosure. Figure 10a presents a target micro-region containing a slanted edge in an enlarged image. Such a slanted edge will cause edge aliasing during viewing. FIG. 10b is a schematic diagram of image edge smoothing adjustment according to a fourth embodiment of the present disclosure. Figure 10b presents an adjustment direction of the image lines were maintained smooth edges 10a of the inclined edge map, and to adjust to the new pixel oblique edge adjacent two new pixel 20012, 20021 sub pixel grayscale value. FIG 10b, the change of the gray level values of the red subpixel R of 20,012 new pixel grayscale value of the red sub-pixel R, wherein the new pixel grayscale value is equal to 20,012 red sub-pixel 11 new pixel 200, 20,022 new pixel grayscale of red sub-pixel value or the average of the two. Furthermore, changing the grayscale value of the blue sub-pixel B 20,021 new pixel grayscale value of the blue sub-pixel 20 021 wherein the new pixel grayscale value is equal to the B subpixel of the blue pixel 200 new 22 20,011 new pixel grayscale of blue sub-pixel value or the average of the two. In addition, FIG. 10c is a schematic diagram of image edge smoothing adjustment according to the fifth embodiment of the present disclosure. Figure 10c differs from place to FIG. 10b, FIG. 10c but also changed the green subpixel G new pixel grayscale value of 20 012, wherein the green subpixel G new grayscale value equal to the new pixel 200 pixel 200 12 11 green sub-pixel gray scale value, average grayscale value, or both of the green sub-pixel 200 new pixel 22. Further, FIG. 10c but also changed the new pixel grayscale value of the green sub-pixel 200 of the G 21, G green subpixel gray level values of the pixels 200 where the new 21 is equal to the green sub-pixel 20,022 new pixel grayscale values, the new 20,011 pixel of green sub-pixel grayscale value of the average, or both. Specifically, after the image edge is smoothly adjusted, the original edge sawing phenomenon is smoothed.
以圖11a至圖11c來對圖像邊緣平滑調整做例 示性的說明，圖11a至圖11c係繪示根據本揭露的第四實施例與第五實施例之圖像邊緣平滑調整的示意圖。圖11a至圖11c與圖10a至圖10c類似，即圖11b為根據本揭露的第四實施例之圖像邊緣平滑調整後的示意圖，且圖11c為根據本揭露的第五實施例之圖像邊緣平滑調整後的示意圖，差別僅為圖11a呈現放大圖像中包含傾斜邊緣的目標微區域，且目標微區域包含4×4個新像素。其餘部分與圖10a至圖10c大致相同，在此不再贅述。 Figure 11a to Figure 11c for image edge smoothing adjustment as an example Illustratively, FIG. 11a to FIG. 11c are schematic diagrams showing image edge smoothing adjustment according to the fourth embodiment and the fifth embodiment of the present disclosure. 11a to 11c are similar to FIG. 10a to FIG. 10c, that is, FIG. 11b is a schematic diagram of image edge smoothing adjustment according to a fourth embodiment of the present disclosure, and FIG. 11c is an image according to a fifth embodiment of the present disclosure. The schematic diagram of the edge smoothing adjustment is only that the target micro-region containing the oblique edge in the enlarged image is presented in FIG. 11a, and the target micro-region contains 4×4 new pixels. The rest is substantially the same as FIG. 10a to FIG. 10c, and details are not described herein again.
以圖12a至圖12c來對圖像邊緣平滑調整做例示性的說明，圖12a至圖12c係繪示根據本揭露的第六實施例與第七實施例之圖像邊緣平滑調整的示意圖。圖12a呈現放大圖像中包含傾斜邊緣的目標微區域。這樣的傾斜邊緣將會造成觀看時，有邊緣鋸齒的現象產生。圖12b為根據本揭露的第六實施例之圖像邊緣平滑調整後的示意圖。圖12b呈現出圖像邊緣平滑調整係保持圖12a之傾斜邊緣的方向，並調整相應於圖12a中的傾斜邊緣的端點的兩個新像素30011、30044的子像素的灰階值，且調整相應於圖12a中的傾斜邊緣的新像素所相鄰的新像素30012、30021、30023、30032、30034、30043的子像素的灰階值。對圖12b而言，將新像素30011的紅色子像素R的灰階值複製至新像素30012的紅色子像素R的灰階值，將新像素30044的藍色子像素B的灰階值複製至新像素30043的藍色子像素B的灰階值。再者，改變了新像素30021、30032的藍色子像素B的灰階值，其中新像素30021的藍色子像素B的灰階值等於新像 素30022的藍色子像素的灰階值，新像素30032的藍色子像素B的灰階值等於新像素30033的藍色子像素的灰階值；改變了新像素30023、30034的紅色子像素R的灰階值，其中新像素30023的紅色子像素R的灰階值等於新像素30022的紅色子像素的灰階值，新像素30034的紅色子像素R的灰階值等於新像素30033的紅色子像素的灰階值。此外，圖12c類似圖11b與圖11c的結合，即類似圖11c，位於奇數列的相應於圖12a中的傾斜邊緣的新像素的相鄰的新像素的兩個子像素(R與G、或G與B)被賦值；即類似圖11b，位於偶數列的相應於圖12a中的傾斜邊緣的新像素的相鄰的新像素的一個子像素(B或R)被賦值。具體而言，經過圖像邊緣平滑調整後，使得原本邊緣鋸齒的現象得以平滑。 An exemplary description of image edge smoothing adjustment is made in FIGS. 12a to 12c, and FIGS. 12a to 12c are schematic diagrams showing image edge smoothing adjustment according to the sixth embodiment and the seventh embodiment of the present disclosure. Figure 12a presents a target micro-region containing a slanted edge in an enlarged image. Such a slanted edge will cause edge aliasing during viewing. FIG. 12b is a schematic diagram of image edge smoothing adjustment according to a sixth embodiment of the present disclosure. Figure 12b presents an adjustment direction of the image lines were maintained smooth edges 12a of the inclined edge map, and to adjust the two new pixel 30011, sub-pixel grayscale value 300 corresponding to the end 44 in FIG inclined edges 12a, And adjusting the grayscale values of the sub-pixels of the new pixels 300 12 , 300 21 , 300 23 , 300 32 , 300 34 , 300 43 adjacent to the new pixels corresponding to the oblique edges in FIG. 12a. FIG 12b, the grayscale values of the red subpixel R 11 new pixel 300 is copied to the new pixel grayscale value of the red sub-pixel R 12 300, a new pixel grayscale blue sub-pixel B 30044 value is copied to a new pixel grayscale value of 30,043 blue subpixel B. Furthermore, a new pixel grayscale value change the blue sub 30021, 30032 B pixels, wherein the blue subpixel B grayscale value of 21 is equal to the new pixel 300 pixel 300 new blue sub pixel gray 22 The gray value of the blue sub-pixel B of the new pixel 300 32 is equal to the gray level value of the blue sub-pixel of the new pixel 300 33 ; the gray-scale value of the red sub-pixel R of the new pixel 300 23 , 300 34 is changed wherein the gray level value of the red subpixel R 23 is equal to the new pixel 300 red sub 30,022 new pixel grayscale values of the pixel gray scale value of the red sub-pixel R pixel 300 of the new pixel 34 is equal to the new red sub 30033 The grayscale value of the pixel. In addition, FIG. 12c is similar to the combination of FIG. 11b and FIG. 11c, that is, similar to FIG. 11c, two sub-pixels (R and G, or adjacent to the adjacent new pixel of the new pixel corresponding to the oblique edge in FIG. 12a of the odd-numbered column G and B) are assigned; that is, similar to Fig. 11b, one sub-pixel (B or R) of the adjacent new pixel of the new pixel corresponding to the oblique edge in Fig. 12a of the even column is assigned. Specifically, after the image edge is smoothly adjusted, the original edge sawing phenomenon is smoothed.
以圖13a至圖13c來對圖像邊緣平滑調整做例示性的說明，圖13a至圖13c係繪示根據本揭露的第八實施例與第九實施例之圖像邊緣平滑調整的示意圖。圖13a呈現放大圖像中包含傾斜邊緣的目標微區域。圖13b為根據本揭露的第八實施例之圖像邊緣平滑調整後的示意圖。圖13b調整相應於傾斜邊緣的新像素所相鄰的一個新像素20012的子像素的灰階值，其中新像素20012的紅色子像素R的灰階值等於新像素20011的紅色子像素的灰階值、20021的紅色子像素的灰階值、20022的紅色子像素的灰階值或三者之平均。此外，圖13c為根據本揭露的第九實施例之圖像邊緣平滑調整後的示意圖。圖13c與圖13b之不同之處為，圖13c更改變了新像素20012的綠色子像素G的灰階值，其中新像 素20012的綠色子像素G的灰階值等於新像素20011的綠色子像素的灰階值、20021的綠色子像素的灰階值、20022的綠色子像素的灰階值或三者之平均。具體而言，經過圖像邊緣平滑調整後，使得原本邊緣鋸齒的現象得以平滑。 The image edge smoothing adjustment is exemplarily illustrated in FIGS. 13a to 13c, and FIGS. 13a to 13c are schematic diagrams showing image edge smoothing adjustment according to the eighth embodiment and the ninth embodiment of the present disclosure. Figure 13a presents a target micro-region containing a slanted edge in an enlarged image. FIG. 13b is a schematic diagram of image edge smoothing adjustment according to an eighth embodiment of the present disclosure. Figure 13b corresponds to the adjusted new pixel adjacent to the beveled edge of the sub-pixel a new pixel grayscale value of 20 012, the grayscale value of the red sub-pixel R pixel 200, wherein the new red sub-pixel 12 is equal to the new pixel 200 11 grayscale value of the red sub pixel 20 021 grayscale values, the red sub-pixel of 20,022 or an average grayscale value of three. In addition, FIG. 13c is a schematic diagram of image edge smoothing adjustment according to the ninth embodiment of the present disclosure. Figure 13c differs from FIG. 13b is a place, FIG. 13c but also changed the green subpixel G new pixel grayscale value of 20 012, wherein the green subpixel G new grayscale value equal to the new pixel 200 pixel 200 12 11 The grayscale value of the green sub-pixel, the grayscale value of the green sub-pixel of 200 21 , the grayscale value of the green sub-pixel of 200 22 , or an average of the three. Specifically, after the image edge is smoothly adjusted, the original edge sawing phenomenon is smoothed.
以圖14a至圖14b來對圖像邊緣平滑調整做例示性的說明，圖14a至圖14b係繪示根據本揭露的第十實施例之圖像邊緣平滑調整的示意圖。圖14a呈現放大圖像中包含傾斜邊緣的目標微區域。圖14b為根據本揭露的第十實施例之圖像邊緣平滑調整後的示意圖。圖14b調整相應於傾斜邊緣的新像素所相鄰的一個新像素40022的子像素的灰階值，並調整相應於傾斜邊緣的一個新像素40021的子像素的灰階值。對圖14b而言，將新像素40021的紅色子像素R的灰階值複製至新像素40022的紅色子像素R的灰階值。具體而言，經過圖像邊緣平滑調整後，使得原本邊緣鋸齒的現象得以平滑。 An exemplary description of the image edge smoothing adjustment is made in FIG. 14a to FIG. 14b, and FIG. 14a to FIG. 14b are schematic diagrams showing image edge smoothing adjustment according to the tenth embodiment of the present disclosure. Figure 14a presents a target micro-region containing a slanted edge in an enlarged image. FIG. 14b is a schematic diagram of image edge smoothing adjustment according to a tenth embodiment of the present disclosure. Figure 14b corresponds to adjust the inclination of the edge adjacent to the new pixel of a new pixel grayscale values of sub-pixels is 40022, and to adjust the inclination of the edge of a new sub-pixel of the pixel 40021 grayscale value. FIG 14b, the grayscale value of the red sub-pixel R pixel 400 new 21 to copy the new pixel grayscale value 400 of the red subpixel R 22. Specifically, after the image edge is smoothly adjusted, the original edge sawing phenomenon is smoothed.
以圖15a至圖15b來對圖像邊緣平滑調整做例示性的說明，圖15a至圖15b係繪示根據本揭露的第十實施例之圖像邊緣平滑調整的示意圖。圖15a至圖15b與圖14a至圖14b類似，差別僅為圖15a與圖14a之像素組成大小不同。其餘部分與圖14a至圖14b大致相同，在此不再贅述。 An exemplary description of the image edge smoothing adjustment is made in FIG. 15a to FIG. 15b. FIG. 15a to FIG. 15b are schematic diagrams showing image edge smoothing adjustment according to the tenth embodiment of the present disclosure. 15a to 15b are similar to FIGS. 14a to 14b, and the difference is only that the pixel composition sizes of FIGS. 15a and 14a are different. The rest is substantially the same as FIG. 14a to FIG. 14b, and details are not described herein again.
綜合上述，本揭露提出一種改善放大圖像品質的方法。透過改善放大圖像之邊緣的清晰度來克服傳統圖像放大方法之放大圖像邊緣模糊的缺陷。更進一步，改善放大圖像之邊緣的平滑度以改善放大圖像邊緣鋸齒的現象。 In summary, the present disclosure proposes a method of improving the quality of an enlarged image. The defect of the edge blur of the enlarged image of the conventional image enlargement method is overcome by improving the sharpness of the edge of the enlarged image. Further, the smoothness of the edges of the enlarged image is improved to improve the phenomenon of aliasing of the edges of the enlarged image.
以上概述了數個實施例的特徵，因此熟習此技藝者可以更了解本揭露的態樣。熟習此技藝者應了解到，其可輕易地把本揭露當作基礎來設計或修改其他的製程與結構，藉此實現和在此所介紹的這些實施例相同的目標及/或達到相同的優點。熟習此技藝者也應可明白，這些等效的建構並未脫離本揭露的精神與範圍，並且他們可以在不脫離本揭露精神與範圍的前提下做各種的改變、替換與變動。 The features of several embodiments are summarized above, and those skilled in the art will be able to understand the aspects of the disclosure. Those skilled in the art will appreciate that the present disclosure can be readily utilized as a basis for designing or modifying other processes and structures, thereby achieving the same objectives and/or achieving the same advantages as the embodiments described herein. . It should be understood by those skilled in the art that the invention may be made without departing from the spirit and scope of the disclosure.
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