CN105741247A - Single-frame data-based real-time image enhancing method - Google Patents
Single-frame data-based real-time image enhancing method Download PDFInfo
- Publication number
- CN105741247A CN105741247A CN201610073916.9A CN201610073916A CN105741247A CN 105741247 A CN105741247 A CN 105741247A CN 201610073916 A CN201610073916 A CN 201610073916A CN 105741247 A CN105741247 A CN 105741247A
- Authority
- CN
- China
- Prior art keywords
- pixel
- real
- image
- time image
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 230000002708 enhancing effect Effects 0.000 title abstract description 7
- 238000005286 illumination Methods 0.000 claims abstract description 19
- 238000000205 computational method Methods 0.000 claims description 7
- 238000009825 accumulation Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000007781 pre-processing Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004907 flux Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000005728 strengthening Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000036039 immunity Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention relates to a single-frame data-based real-time image enhancing method. The method comprises the following steps: obtaining single-frame low-illumination image data; calculating the pixel mean value and the all-order moment values of the low-illumination image data; judging whether the image data are too dark or not; carrying out mask enhancing on all the pixel points of the image; and replacing original pixel value with mask-enhanced pixel value to obtain enhanced image data. By means of the method disclosed by the invention, the real-time image enhancing can be carried out on the data of the single-frame image per se under the circumstance of low illumination and the signal-to-noise ratio of the image is increased at the same time.
Description
Technical field
The present invention relates to technical field of image processing, particularly relate to a kind of Real-time image enhancement method based on frame data.
Background technology
At present, the key technology of image procossing, machine vision and safety monitoring system is directed to a key problem, namely the image obtained under low-light (level) is carried out real-time enhancing.Whether system can get effective image information when low-light (level), directly affect System Back-end and user to the subsequent treatment of image and identification, particularly in machine vision, monitor in real time, intelligent security guard field, image enhaucament under low-light (level) there is very big application demand, therefore, real-time under low-light (level) algorithm for image enhancement becomes one of key technology in image procossing, machine vision, the research of monitoring safety-security area.
When carrying out real-time image enhaucament, exist and to adapt to that low-light (level) requires, image real-time, enhanced has the demands such as high signal to noise ratio, and do not rely on hardware, need to meet availability in actual use strong, do not lose the requirement of key message.
In order to realize the target of Real-time image enhancement under low-light (level), it is possible to use hardware increases time of exposure and luminous flux or the method using interframe accumulation on algorithm.The method increasing time of exposure on hardware is to increase the two field picture time of acquisition, and many stored charges on hardware, the method increasing luminous flux is to increase lens aperture so that have more light to enter, hardware obtains electric charge more.The signal noise ratio (snr) of image that above method obtains is relatively low, and even obtains less than correct images at the real-time situation hypograph quality degradation obtaining the quickly object of movement, and hardware-dependent is serious.And the method for interframe accumulation is to utilize the multiple image corresponding pixel points continuously acquired to carry out pixel value accumulation, the method that software realizes many stored charges, it is low that this method equally exists signal noise ratio (snr) of image, it is impossible to obtains the situation of rapid moving object effective image.
Summary of the invention
The technical problem to be solved is to provide a kind of Real-time image enhancement method based on frame data, can be in low light situations, the data of single-frame images own are used to carry out Real-time image enhancement process, improving the signal to noise ratio of image, subsequent treatment and identification for System Back-end and user provide reliable data basis simultaneously.
The technical solution adopted for the present invention to solve the technical problems is: provides a kind of Real-time image enhancement method based on frame data, comprises the following steps:
(1) single frames low-illumination image data is obtained；
(2) pixel average and each rank square value of low-illumination image data are calculated；
(3) judge that whether view data is excessively dark；
(4) each pixel of image is carried out mask enhancement process；
(5) use through the enhanced pixel value replacement original pixel value of mask, obtain enhanced view data.
Described step (1) also includes that the low-illumination image data obtained is filtered pretreatment and removes the step of noise spot.
In described step (2), the computational methods of pixel average are:The computational methods of each rank square value are:Wherein, x represents that the total length of view data, y represent the overall width of view data, and (i, j) represents the view data of the i-th row jth row to S, and n is exponent number.
By the method for setting threshold value, described step (3) judges that whether view data is excessively dark.
Described step (4) is particularly as follows: for each pixel of image, centered by it, select suitable mask window to process according to pixel coordinate situation, utilize the vicinity points within mask window, calculate its enhanced pixel value.
Described mask window has two ways, with image upper left angle point for coordinate (1,1) point, when the transverse and longitudinal coordinate only one of which of pixel is even number situation, selects the first mask window, and other situations select the second mask window；Wherein, the neighbor point pixel of the first mask window is the pixel on the four direction of initial point pixel upper and lower, left and right；The neighbor point pixel of the second mask window is the pixel on initial point pixel upper left, lower-left, upper right, bottom right four direction.
Described step (5) also includes to the step obtaining enhanced view data and be filtered removing distortion point and noise spot.
Beneficial effect
Owing to have employed above-mentioned technical scheme, the present invention is compared with prior art, have the following advantages that and good effect: present invention achieves in low light situations, the data of single-frame images own are used to carry out Real-time image enhancement process, improving the signal to noise ratio of image, subsequent treatment and identification for System Back-end and user provide reliable data basis simultaneously.The real-time of feasible system of the present invention, and be prone to transplant, it is particularly suitable for machine vision, monitors the environment that safety-security area this real-time accuracy requirement is higher.
Accompanying drawing explanation
Fig. 1 is the flow chart of the present invention；
Fig. 2 is the flow chart obtaining single frames low-illumination image data in the present invention；
Fig. 3 is that the present invention falls into a trap and calculates the flow chart of the average of image pixel data and each rank square value；
Fig. 4 is the flow chart clicking on line mask enhancement process in the present invention pixel-by-pixel；
Fig. 5 is the schematic diagram of mask window mode 1 in the present invention；
Fig. 6 is the schematic diagram of mask window mode 2 in the present invention；
Fig. 7 is that the present invention uses front and back effect contrast figure.
Detailed description of the invention
Below in conjunction with specific embodiment, the present invention is expanded on further.Should be understood that these embodiments are merely to illustrate the present invention rather than restriction the scope of the present invention.In addition, it is to be understood that after having read the content that the present invention lectures, the present invention can be made various changes or modifications by those skilled in the art, and these equivalent form of values fall within the application appended claims limited range equally.
Fig. 1 is the flow chart of a kind of Real-time image enhancement algorithm based on frame data of the present invention, as it is shown in figure 1, described method, comprises the following steps:
Step 100: obtain single frames low-illumination image data；
Step 200: calculate average and each rank square value of image pixel data；
Step 300: judge that whether image is excessively dark；
Step 400: click on line mask enhancement process pixel-by-pixel；
Step 500: obtain view data after strengthening.
Fig. 2 is the structural representation obtaining single frames low-illumination image data in the present invention, describes acquisition low-illumination image data and Filter Preprocessing Method.
Step 101: obtain the most original single frames low-illumination image data P.
Step 102: the original single frames low-illumination image data P obtained is filtered pretreatment, removes video camera under interference noise, particularly low-light (level) and increases the interference noise point that time of exposure produces.Filter Preprocessing Method generally takes M*M medium filtering, wherein, and M=3,5 or other numerical value.
Step 103: obtain view data S after obtaining filter preprocessing.
Fig. 3 is that the present invention falls into a trap and calculates the structural representation of the average of image pixel data and each rank square value, describes and obtains the method judging the whether excessively dark eigenvalue of image illumination.
Step 201: all or part of data of use view data S carry out the calculating of the meansigma methods of image pixel value, it is assumed that the data size participating in calculating is x*y, and wherein the computational methods of meansigma methods are:Wherein, x represents that the total length of view data, y represent the overall width of view data, and (i j) represents the view data of the i-th row jth row to S.
Step 202: using all or part of data of view data S to carry out the meansigma methods of image pixel value and the calculating of each rank square, the computational methods of n rank square value are:Wherein n is exponent number.Generally we only calculate to third moment and just can embody desirable characteristics, and can reduce amount of calculation.
Step 203: use the average obtained and each rank square value to carry out the calculating of image illumination judging characteristic value.
In step 300, the average obtained and each rank metric calculation eigenvalue is used to carry out image illumination judgement, and with K for judgment threshold, it is judged that method is as follows:When illumination is relatively low, then it is assumed that this image needs to carry out enhancement process.
Fig. 4 is that the present invention is a kind of based on the structural representation clicking on line mask enhancement process in the Real-time image enhancement algorithm of frame data pixel-by-pixel, describes the method using specific mask mode that image is carried out pointwise enhancement process.
Step 401: carry out pointwise selection, is operated centered by each pixel participating in calculating.
Step 402: use selected pixel, judge its coordinate type, suitable mask mode is selected to carry out strengthening calculating, mask window has two ways to wait to select, with image upper left angle point for coordinate (1,1) point, when the transverse and longitudinal coordinate only one of which of pixel is even number situation, selection mode 1, other situation selection modes 2.Mask window mode 1 and mask window mode 2 are different in the mode obtaining neighbor point pixel.The neighbor point pixel of mask window mode 1 is the pixel on the four direction of initial point pixel upper and lower, left and right, sees Fig. 5；The neighbor point pixel of mask window mode 2 is the pixel on initial point pixel upper left, lower-left, upper right, bottom right four direction, sees Fig. 6.
Step 403: the enhancing carrying out this central pixel point calculates, and utilizes the vicinity points within mask window, calculates its enhanced pixel value, it is assumed that the adjacent pixels point number participating in calculating is m, and the computational methods of enhanced pixel value V are: Wherein, kiProportional weight for each adjacent pixels point participating in and calculating.
In step 500, use and replace original pixel value through the enhanced pixel value of mask, obtain enhanced view data, be filtered removing distortion point and noise spot to obtaining enhanced view data.Fig. 7 is that the present invention is a kind of based on effect contrast figure before and after the use of the Real-time image enhancement algorithm of frame data.
As can be seen here, a kind of Real-time image enhancement algorithm based on frame data of the present invention, it is achieved that be rapidly performed by image real time enhancing when low-light (level), improve signal noise ratio (snr) of image simultaneously.Being made without substantial amounts of data acquisition, strong interference immunity, speed are fast, it may be achieved the real-time of system, are particularly suitable at the higher environment of machine vision, this real-time effectiveness requirement of intelligent security guard field.
Claims (7)
1. the Real-time image enhancement method based on frame data, it is characterised in that comprise the following steps:
(1) single frames low-illumination image data is obtained；
(2) pixel average and each rank square value of low-illumination image data are calculated；
(3) judge that whether view data is excessively dark；
(4) each pixel of image is carried out mask enhancement process；
(5) use through the enhanced pixel value replacement original pixel value of mask, obtain enhanced view data.
2. the Real-time image enhancement method based on frame data according to claim 1, it is characterised in that described step (1) also includes that the low-illumination image data obtained is filtered pretreatment and removes the step of noise spot.
3. the Real-time image enhancement method based on frame data according to claim 1, it is characterised in that in described step (2), the computational methods of pixel average are:The computational methods of each rank square value are:Wherein, x represents that the total length of view data, y represent the overall width of view data, and (i, j) represents the view data of the i-th row jth row to S, and n is exponent number.
4. the Real-time image enhancement method based on frame data according to claim 1, it is characterised in that judge that whether view data is excessively dark by the method for setting threshold value in described step (3).
5. the Real-time image enhancement method based on frame data according to claim 1, it is characterized in that, described step (4) is particularly as follows: for each pixel of image, centered by it, suitable mask window is selected to process according to pixel coordinate situation, utilize the vicinity points within mask window, calculate its enhanced pixel value.
6. the Real-time image enhancement method based on frame data according to claim 5, it is characterized in that, described mask window has two ways, with image upper left angle point for coordinate (1,1) point, when the transverse and longitudinal coordinate only one of which of pixel is even number situation, selecting the first mask window, other situations select the second mask window；Wherein, the neighbor point pixel of the first mask window is the pixel on the four direction of initial point pixel upper and lower, left and right；The neighbor point pixel of the second mask window is the pixel on initial point pixel upper left, lower-left, upper right, bottom right four direction.
7. the Real-time image enhancement method based on frame data according to claim 1, it is characterised in that also include in described step (5) the step obtaining enhanced view data and be filtered removing distortion point and noise spot.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610073916.9A CN105741247A (en) | 2016-02-02 | 2016-02-02 | Single-frame data-based real-time image enhancing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610073916.9A CN105741247A (en) | 2016-02-02 | 2016-02-02 | Single-frame data-based real-time image enhancing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105741247A true CN105741247A (en) | 2016-07-06 |
Family
ID=56245622
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610073916.9A Pending CN105741247A (en) | 2016-02-02 | 2016-02-02 | Single-frame data-based real-time image enhancing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105741247A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104182998A (en) * | 2014-08-14 | 2014-12-03 | 深圳市云宙多媒体技术有限公司 | Self-adaption image brightness rendering method and device |
| CN104809712A (en) * | 2015-05-15 | 2015-07-29 | 河海大学常州校区 | Rapid image repairing method based on rough set |
| JP2015150185A (en) * | 2014-02-14 | 2015-08-24 | コニカミノルタ株式会社 | X-ray imaging system and image processing method |
-
2016
- 2016-02-02 CN CN201610073916.9A patent/CN105741247A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015150185A (en) * | 2014-02-14 | 2015-08-24 | コニカミノルタ株式会社 | X-ray imaging system and image processing method |
| CN104182998A (en) * | 2014-08-14 | 2014-12-03 | 深圳市云宙多媒体技术有限公司 | Self-adaption image brightness rendering method and device |
| CN104809712A (en) * | 2015-05-15 | 2015-07-29 | 河海大学常州校区 | Rapid image repairing method based on rough set |
Non-Patent Citations (2)
| Title |
|---|
| 胡琼: "基于低照度图像的人脸检测方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 陈健美等: "《数字图像处理与分析》", 31 March 2015 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Huang et al. | An advanced single-image visibility restoration algorithm for real-world hazy scenes | |
| CN105046677B (en) | A kind of enhancing treating method and apparatus for traffic video image | |
| JP7256902B2 (en) | Video noise removal method, apparatus and computer readable storage medium | |
| CN105631831B (en) | Video image enhancing method under the conditions of a kind of haze | |
| WO2019221013A4 (en) | Video stabilization method and apparatus and non-transitory computer-readable medium | |
| CN102521813B (en) | Infrared image adaptive enhancement method based on dual-platform histogram | |
| CN108257165B (en) | Image stereo matching method and binocular vision equipment | |
| US9478016B2 (en) | Image restoration method and image processing apparatus using the same | |
| CN109359593B (en) | Rain and snow environment picture fuzzy monitoring and early warning method based on image local grid | |
| CN102306307B (en) | Positioning method of fixed point noise in color microscopic image sequence | |
| US9053355B2 (en) | System and method for face tracking | |
| CN110992288B (en) | Video image blind denoising method used in mine shaft environment | |
| CN104767913B (en) | A kind of adaptive video denoising system of contrast | |
| CN113034634A (en) | Adaptive imaging method, system and computer medium based on pulse signal | |
| CN104794727A (en) | Symmetry based fast calibration method of PSF (Point Spread Function) for single-lens imaging calculation | |
| CN104700405A (en) | Foreground detection method and system | |
| CN105046670A (en) | Image rain removal method and system | |
| CN102509311B (en) | Motion detection method and device | |
| Pan et al. | Single-image dehazing via dark channel prior and adaptive threshold | |
| CN102724541A (en) | Intelligent diagnosis and recovery method for monitoring images | |
| CN112307895A (en) | Crowd gathering abnormal behavior detection method under community monitoring scene | |
| CN101877135A (en) | Moving target detecting method based on background reconstruction | |
| CN105741247A (en) | Single-frame data-based real-time image enhancing method | |
| CN110838120A (en) | Weighting quality evaluation method of asymmetric distortion three-dimensional video based on space-time information | |
| CN112911160A (en) | Image shooting method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160706 |