CN109272489A - Inhibit the method for detecting infrared puniness target with multiple dimensioned local entropy based on background - Google Patents

Inhibit the method for detecting infrared puniness target with multiple dimensioned local entropy based on background Download PDF

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CN109272489A
CN109272489A CN201810956335.9A CN201810956335A CN109272489A CN 109272489 A CN109272489 A CN 109272489A CN 201810956335 A CN201810956335 A CN 201810956335A CN 109272489 A CN109272489 A CN 109272489A
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CN109272489B (en
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武斌
侯敏
李鹏
王晓鹏
鲍丹
马聪聪
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Xi'an Thunder Driven Electronic Technology Co ltd
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Xidian University
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    • G06T7/0002—Inspection of images, e.g. flaw detection
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
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Abstract

The present invention proposes a kind of method for detecting infrared puniness target inhibited based on background with multiple dimensioned local entropy, and infrared image is normalized first；Secondly guiding figure filter filtering is carried out to infrared image, the image after background inhibits is obtained after difference；Then by calculating multiple dimensioned local entropy weight figure, the maximum value of different scale local entropy notable figure is taken to same location of pixels, obtains final local entropy weight figure；Image after inhibiting later to background is multiplied with local entropy notable figure, obtains the notable figure of infrared small target；Finally notable figure is filtered using Susan filter, removes isolated bright spot, the non-zero region of image is Small object region after processing.The present invention can be used for detecting the Weak target in infrared image, can effectively improve the detection accuracy of Infrared Images target.

Description

Inhibit the method for detecting infrared puniness target with multiple dimensioned local entropy based on background
Technical field
The invention belongs to technical field of image processing, further relate under complex background in infrared image processing field A kind of method for detecting infrared puniness target inhibited based on background with multiple dimensioned local entropy.
Background technique
Infrared small target detection has been widely used in multiple fields, such as infrared object tracking, precise guidance and long-range morning Phase early warning etc..Due to the complexity of imaging circumstances and the characteristic of infrared picture itself, infrared small target stock size is small, signal It is weak, background is complicated so that its detection and tracking is extremely difficult.Therefore, small IR targets detection is always infrared image processing The hot and difficult issue of area research.
Research in terms of human eye vision attention mechanism in recent years, is widely used in the detection of infrared small target. Visual attention model is a kind of biological heuristic models proposed earliest by Koch etc. in 1987.Vision noticing mechanism is a mankind To the important psychological regulation mechanism that most interested, most relevant range is selected and paid attention in scene.In 1998, Itti Et al. first proposed a kind of visual attention model, and model is applied to the image detection of visible light, receives the wide of people General concern.The it is proposeds such as Itti are a kind of conspicuousness detection algorithms based on center-surround, and the main thought of algorithm is By the brightness of image, color and three, direction feature information extraction, and multiple dimensioned gaussian pyramid is formed, to obtain significant Figure carries out target detection.Cheng in 2011 etc. proposes a kind of conspicuousness detection algorithm based on global contrast, mentions in algorithm Spatial relationship is also extracted as a kind of feature, causes extensive concern by the concept of local contrast out.Later Chen etc. proposes a kind of local contrast algorithm (LCM), and vision significance is shown up prominently in terms of Dim targets detection.Algorithm is logical It crosses central area and 8 neighborhood of surrounding compares to obtain local contrast figure, then carry out target detection again, but vulnerable to influence of noise, it is empty Alert rate is higher.Han etc. proposes a kind of improved LCM (ILCM) algorithm, algorithm is first by image procossing on the basis of LCM algorithm The maximum value in LCM algorithm is replaced at sub-pixel block, then with mean value, so that false alarm rate decreases, but still vulnerable to noise shadow It rings.
Summary of the invention
For the above the deficiencies in the prior art, the present invention proposes a kind of to inhibit infrared with multiple dimensioned local entropy based on background Detection method of small target, to improve detection performance.The present invention can be used for detecting the Weak target in infrared image, energy The effective detection accuracy for improving Infrared Images target.
Realizing technical thought of the invention is: firstly, infrared image is normalized；Secondly, to infrared image Guiding figure filter filtering is carried out, the image after background inhibits is obtained after difference；Then, by calculating multiple dimensioned local entropy weight Value figure, the maximum value of different scale local entropy notable figure is taken to same location of pixels, obtains final local entropy weight figure；It Afterwards, the image after inhibiting to background is multiplied with local entropy notable figure, obtains the notable figure of infrared small target；Finally, utilizing Susan filter is filtered notable figure, removes isolated bright spot, and the non-zero region of image is Small object area after processing Domain.
The technical solution of the present invention is as follows:
A kind of method for detecting infrared puniness target inhibited based on background with multiple dimensioned local entropy, it is characterised in that: The following steps are included:
Step 1: one infrared image I to be detected of input；
Step 2: the image I of input being converted into gray level image and is normalized, image I is obtainedin；
Step 3: background inhibition is carried out by following steps:
Step 3.1: using guiding figure filter, to the infrared image I after normalizationinIt is filtered, after obtaining filtering Class background image IB；
Step 3.2: by the infrared image I after normalizationinWith the class background image I obtained after filteringBDifference is carried out, is obtained Image I after inhibiting to backgroundBS；
Step 4: multiple dimensioned local entropy is calculated by following steps:
Step 4.1: selected window scale is the window of m to the infrared image I after normalizationinCarry out local entropy operation；
Step 4.2: replacing the value of window center pixel with the local entropy being calculated, obtain the part that window size is m Entropy weight figure Wm；
Step 4.3: changing the value of window size m, repeat step 4.1 and step 4.2, obtain under different windows scale Local entropy weight figure；
Step 4.4: for a certain location of pixels of local entropy weight figure, taking in all local entropy weight figures in the pixel position The maximum value for setting place forms final local entropy weight figure ILE；
Step 5: the image I after inhibiting to backgroundBSWith local entropy notable figure ILEIt is multiplied, obtains infrared small target Notable figure S；
Step 6: target detection is carried out by following procedure:
Step 6.1: notable figure S being filtered using Susan filter to obtain image Iout；
Step 6.2: removal image IoutIn isolated noise point after, obtain image IoutIn non-zero region be Small object Region.
Further preferred embodiment, a kind of small IR targets detection inhibited based on background with multiple dimensioned local entropy Method, it is characterised in that: using guiding figure filter in step 3.1, to the infrared image I after normalizationinIt is filtered, obtains Class background image I after filteringBDetailed process are as follows:
With guiding figure filter to the infrared image I after normalizationinIt is filtered, obtains background image:
Wherein, l is integer, and w indicates window size, FwIndicate that guiding figure filter when window size is w, * indicate filter Wave operation；
Then change window size and repeat filtering operation, obtain multiple background images；Finally make even to multiple background images Mean value obtains final class background image IB:
。
Further preferred embodiment, a kind of small IR targets detection inhibited based on background with multiple dimensioned local entropy Method, it is characterised in that: step 4.1 to 4.2 obtains the local entropy weight figure W that window size is mmProcess are as follows:
Selected window scale is the window of m to the infrared image I after normalization firstinFrom top to bottom, it from left to right carries out Window is mobile: if image IinSize be M × N, by the neighborhood definition of pixel (x, y) are as follows:
θm=(p, q) | max (| p-x |, | q-y |)≤m, m=1,2 ..., L
Wherein m, L are positive integer, and m indicates the scale size of window, and (p, q) indicates picture in the neighborhood of pixel (x, y) The coordinate position of vegetarian refreshments；
Then the local entropy for calculating pixel (x, y) neighborhood, pixel (x, y) neighborhood local entropy when window size is m Is defined as:
Wherein D is indicated when window size is m, neighborhood θmIn include D kind gray value, i-th kind of gray value is fi, i=1, 2 ..., D, f (x, y) indicate pixel (x, y) corresponding gray value,piIt is probability density function, niFor i-th kind of ash The number of pixels of angle value.
Further preferred embodiment, a kind of small IR targets detection inhibited based on background with multiple dimensioned local entropy Method, it is characterised in that: the image I after inhibiting in step 5 to backgroundBSWith local entropy notable figure ILEIt is multiplied, obtains infrared The detailed process of the notable figure S of Small object are as follows:
The value of notable figure at point (x, y) is indicated first are as follows:
S (x, y)=IBS(x,y)×ILE(x,y)
Wherein ILE(x, y) indicates the multiple dimensioned local entropy weight of pixel (x, y), IBS(x, y) indicates pixel (x, y) Value after background inhibition, S (x, y) indicate the value of the notable figure of pixel (x, y) infrared small target；
Secondly value minus in notable figure is set to zero:
。
Beneficial effect
The present invention compared with prior art, has the advantage that
1) guiding figure filter is applied in the scene for extracting image background information for the first time, and background is obtained by difference Infrared target notable figure after inhibition；
2) Small object in image is highlighted using the notable figure that improved local entropy is constituted, and uses multiple dimensioned office Portion's entropy adapts to the dynamic change of Small object size under the conditions of different background, detection error caused by avoiding window selection improper；
3) final notable figure is to weight, the deficiency of the single treatment mode avoided, while passing through Susan filter Processing eliminate influence of the background clutter to target detection.
Additional aspect and advantage of the invention will be set forth in part in the description, and will partially become from the following description Obviously, or practice through the invention is recognized.
Detailed description of the invention
Above-mentioned and/or additional aspect of the invention and advantage will become from the description of the embodiment in conjunction with the following figures Obviously and it is readily appreciated that, in which:
Fig. 1 is the basic procedure of small IR targets detection.
Fig. 2 is the result of small IR targets detection: first is classified as original infrared input picture；Second is classified as by place The Saliency maps picture obtained after reason；Third is classified as testing result.
Specific embodiment
The embodiment of the present invention is described below in detail, the embodiment is exemplary, it is intended to it is used to explain the present invention, and It is not considered as limiting the invention.
Referring to Fig.1, one of the present embodiment inhibits the small IR targets detection with multiple dimensioned local entropy based on background Method the following steps are included:
Step 1: one infrared image I to be detected of input.
Step 2: the image I of input being converted into gray level image and is normalized, image I is obtainedin.I.e. to infrared Image I carries out gray scale and normalized, and the gray value of infrared image I is normalized between 0-1, image I is obtainedin。
Step 3: background inhibits.
I.e. using guiding figure filter to the infrared image I after normalizationinIt is filtered.Guiding figure filter is one The relatively new type image filter of kind, it can not only keep the edge feature of image smoothing well, and can also be preferable Retain the information of edge neighborhood in image.It is different in general filter, and operation efficiency is unrelated with the size of core, thus Many fields are widely used, such as: image defogging scratches figure, except make an uproar etc..
When being oriented among treatment process of the figure filter applied to infrared image, due to infrared small target in infrared image Itself the characteristics of, it may be assumed that pixel shared by infrared target is considerably less, and the brightness of infrared small target is relatively high etc..It will be infrared Image passes through after guiding figure filtering, can preferably save the background information etc. in image, finally, by making with original image Difference obtains the image after background inhibits.
Step 3.1: using guiding figure filter, to the infrared image I after normalizationinIt is filtered, after obtaining filtering Class background image IB.Detailed process are as follows:
With guiding figure filter to the infrared image I after normalizationinIt is filtered, obtains background image:
Wherein, l is integer, and w indicates window size, FwIndicate that guiding figure filter when window size is w, * indicate filter Wave operation.
Then change window size and repeat filtering operation, obtain multiple background images；Finally make even to multiple background images Mean value obtains final class background image IB:
Step 3.2: by the infrared image I after normalizationinWith the class background image I obtained after filteringBDifference is carried out, is obtained Image I after inhibiting to backgroundBS；IBS=Iin-IB。
Step 4: multiple dimensioned local entropy calculates:
In one width infrared image, Weak target can regard a singular point incompatible with ambient enviroment, target area as Target context and background can be different from by the difference of gray value.But when the complicated noise jamming of background is strong, small mesh Mark is easy to be submerged, and the difference of gray value is not enough to detect Small object at this time, may cause the case where inspection does not measure Small object Occur.And window local entropy is selected then to avoid the generation of such case.
The information content contained by different size of window is proportional to the numerical value of the entropy of window in image, the letter that window includes Breath content is influenced by the selection (i.e. window size) of window.In order to adapt to the detection of different size Small objects, weaken clutter Bring adverse effect, significantly counteracting residual noise.Local entropy operation is carried out using different size of window, is videlicet carried out Calculate multiple dimensioned local entropy.
Step 4.1: selected window scale is the window of m to the infrared image I after normalizationinCarry out local entropy operation.
Step 4.2: replacing the value of window center pixel with the local entropy being calculated, obtain the part that window size is m Entropy weight figure Wm；For edge pixel point using corresponding edge pixel filling window come the local entropy of calculating field.
Step 4.1 to 4.2 obtain window size be m local entropy weight figure WmProcess are as follows:
Selected window scale is the window of m to the infrared image I after normalization firstinFrom top to bottom, it from left to right carries out Window is mobile: if image IinSize be M × N, by the neighborhood definition of pixel (x, y) are as follows:
θm=(p, q) | max (| p-x |, | q-y |)≤m, m=1,2 ..., L
Wherein m, L are positive integer, and m indicates the scale size of window, and (p, q) indicates picture in the neighborhood of pixel (x, y) The coordinate position of vegetarian refreshments.
Then the local entropy for calculating pixel (x, y) neighborhood, pixel (x, y) neighborhood local entropy when window size is m Is defined as:
Wherein D is indicated when window size is m, neighborhood θmIn include D kind gray value, i-th kind of gray value is fi, i=1, 2 ..., D, f (x, y) indicate pixel (x, y) corresponding gray value,piIt is probability density function, niFor i-th kind of ash The number of pixels of angle value.
Step 4.3: changing the value of window size m, repeat step 4.1 and step 4.2, obtain under different windows scale Local entropy weight figure.
Step 4.4: for a certain location of pixels (x, y) of local entropy weight figure, taking in all local entropy weight figures at this Maximum value at location of pixels (x, y) forms final local entropy weight figure ILE；
ILE(x, y)=max | Wm(x, y) |, m=1,2 ..., L }
Step 5: the notable figure of infrared small target:
Vision significance measurement is the key link in the image partition method of view-based access control model conspicuousness, mainly calculates image In each pixel conspicuousness, as a result with indicating with the equal-sized width gray level image of original input picture, referred to as notable figure. Wherein each pixel value represents the saliency value of corresponding position pixel in original image, is worth bigger explanation pixel in original graph It is more significant as in, more it is easy to get the attention of observer.
Image I after inhibiting to backgroundBSWith local entropy notable figure ILEIt is multiplied, obtains the notable figure of infrared small target S.Detailed process are as follows:
The value of notable figure can indicate first at point (x, y) are as follows:
S (x, y)=IBS(x,y)×ILE(x,y)
Wherein ILE(x, y) indicates the multiple dimensioned local entropy weight of pixel (x, y), IBS(x, y) indicates pixel (x, y) Value after background inhibition, S (x, y) indicate the value of the notable figure of pixel (x, y) infrared small target.
Secondly value minus in notable figure is set to zero:
Then whole image can be traversed by, which handling by multiple dimensioned local entropy, realizes highlighting while inhibiting background to Small object Noise improves the adaptability of algorithm and the robustness of multiple dimensioned local contrast.
Step 6: target detection:
Susan filter is a kind of detection method based on template window, mainly by each pixel point of image The place of setting establishes a window, by comparing neighborhood of pixel points with its center similarity degree, to carry out image procossing.Used ginseng Number is very few, calculation amount and storage capacity is required low.Mainly to be directed to edge detection and Corner Detection, it is also used for making an uproar Sound is eliminated.The non-maxima suppression of edge detection in Susan filter is removed, is used to by the characteristic based on infrared small object Carry out target detection.
Target detection is carried out by following procedure:
Step 6.1: notable figure S being filtered using Susan filter to obtain image Iout；
Step 6.2: statistics IoutNon-zero pixels number in image is directly entered step 6.3 if number is 1, no Then carry out rejecting IoutThe operation of isolated noise point in image, even image IoutIn 8 neighborhoods around certain non-zero pixels point Pixel value is 0, then it is assumed that the pixel is isolated noise spot, carries out rejecting processing to it, its value is set to 0.
Step 6.3: taking image IoutIn non-zero region be Small object region.
Although the embodiments of the present invention has been shown and described above, it is to be understood that above-described embodiment is example Property, it is not considered as limiting the invention, those skilled in the art are not departing from the principle of the present invention and objective In the case where can make changes, modifications, alterations, and variations to the above described embodiments within the scope of the invention.

Claims (4)

1. a kind of inhibited and the method for detecting infrared puniness target of multiple dimensioned local entropy based on background, it is characterised in that: including with Lower step:
Step 1: one infrared image I to be detected of input；
Step 2: the image I of input being converted into gray level image and is normalized, image I is obtainedin；
Step 3: background inhibition is carried out by following steps:
Step 3.1: using guiding figure filter, to the infrared image I after normalizationinIt is filtered, the class after being filtered Background image IB；
Step 3.2: by the infrared image I after normalizationinWith the class background image I obtained after filteringBDifference is carried out, is carried on the back Image I after scape inhibitionBS；
Step 4: multiple dimensioned local entropy is calculated by following steps:
Step 4.1: selected window scale is the window of m to the infrared image I after normalizationinCarry out local entropy operation；
Step 4.2: replacing the value of window center pixel with the local entropy being calculated, obtain the local entropy weight that window size is m Value figure Wm；
Step 4.3: changing the value of window size m, repeat step 4.1 and step 4.2, obtain the part under different windows scale Entropy weight figure；
Step 4.4: for a certain location of pixels of local entropy weight figure, taking in all local entropy weight figures in the pixel position Maximum value, form final local entropy weight figure ILE；
Step 5: the image I after inhibiting to backgroundBSWith local entropy notable figure ILEIt is multiplied, obtains the notable figure of infrared small target S；
Step 6: target detection is carried out by following procedure:
Step 6.1: notable figure S being filtered using Susan filter to obtain image Iout；
Step 6.2: removal image IoutIn isolated noise point after, obtain image IoutIn non-zero region be Small object area Domain.
2. a kind of method for detecting infrared puniness target inhibited based on background with multiple dimensioned local entropy according to claim 1, It is characterized by: using guiding figure filter in step 3.1, to the infrared image I after normalizationinIt is filtered, is filtered Class background image I laterBDetailed process are as follows:
With guiding figure filter to the infrared image I after normalizationinIt is filtered, obtains background image:
IBw=Iin*Fw, w=w1,w2,……,wl
Wherein, l is integer, and w indicates window size, FwIndicate that guiding figure filter when window size is w, * indicate filtering fortune It calculates；
Then change window size and repeat filtering operation, obtain multiple background images；Finally multiple background images are averaged Obtain final class background image IB:
。
3. a kind of method for detecting infrared puniness target inhibited based on background with multiple dimensioned local entropy according to claim 1, It is characterized by: step 4.1 to 4.2 obtain window size be m local entropy weight figure WmProcess are as follows:
Selected window scale is the window of m to the infrared image I after normalization firstinFrom top to bottom, window is from left to right carried out It is mobile: if image IinSize be M × N, by the neighborhood definition of pixel (x, y) are as follows:
θm=(p, q) | max (| p-x |, | q-y |)≤m, m=1,2 ..., L
Wherein m, L are positive integer, and m indicates the scale size of window, and (p, q) indicates pixel in the neighborhood of pixel (x, y) Coordinate position；
Then the local entropy for calculating pixel (x, y) neighborhood, the definition of pixel (x, y) neighborhood local entropy when window size is m Are as follows:
Wherein D is indicated when window size is m, neighborhood θmIn include D kind gray value, i-th kind of gray value is fi, i=1,2 ..., D, f (x, y) indicate pixel (x, y) corresponding gray value,piIt is probability density function, niFor i-th kind of gray value Number of pixels.
4. a kind of method for detecting infrared puniness target inhibited based on background with multiple dimensioned local entropy according to claim 1, It is characterized by: the image I after inhibiting in step 5 to backgroundBSWith local entropy notable figure ILEIt is multiplied, obtains infrared small mesh The detailed process of target notable figure S are as follows:
The value of notable figure at point (x, y) is indicated first are as follows:
S (x, y)=IBS(x,y)×ILE(x,y)
Wherein ILE(x, y) indicates the multiple dimensioned local entropy weight of pixel (x, y), IBS(x, y) indicates the suppression of pixel (x, y) background Value after system, S (x, y) indicate the value of the notable figure of pixel (x, y) infrared small target；
Secondly value minus in notable figure is set to zero:
。
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