CN103714520A - Digital video image enhancement achieving system and method based on FPGA - Google Patents
Digital video image enhancement achieving system and method based on FPGA Download PDFInfo
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Abstract
The invention discloses a digital video image enhancement achieving system and method based on an FPGA. The digital video image enhancement achieving method based on the FPGA mainly comprises the steps of dehazing processing on a current frame image and anti-reflection processing on the current frame image, wherein the step of dehazing processing on the video image is achieved through a dark channel prior dehazing algorithm, and a good dehazing effect can be achieved; the step of anti-reflection processing on the current frame image is achieved through an image histogram equalization algorithm, and the effect of converting a low-illumination-level gray image into a clear image can be achieved. By the adoption of the image dehazing process and the image anti-reflection process, the brightness, contrast ratio and color saturation of the image can be improved, and the image can be clearer, transparent and saturated in color.
Description
Technical field
The present invention relates to Digital Video Processing technical field, relate in particular to and a kind ofly based on FPGA, realize digital video image and strengthen system and method thereof.
Background technology
Image enhancement technique is widely used already at other field: as the post-processed of photography for photo, the professional post-processed softwares such as Photoshop, lightroom have the function of powerful figure image intensifying; A lot of video players are also integrated in recent years image enhancement functions, as the left eye enhancing of the sharpening of KMplayer, YUV diffusion, Auto Laves control etc., MPC.It is realized effect and can be used for reference by field of video monitoring.Field of video monitoring also starts to notice the value of image enhancement technique application in recent years, the image directly getting from imageing sensor is often because of low quality and do not meet the requirement of subsequent treatment and application, need to be through treatment steps such as various processing and conversion to improve quality, be beneficial to processing and the application of subsequent step, and meet the mankind's vision or application demand.
In prior art, also there are some image enchancing methods, as the applying date is: April 15 in 2010, disclosed application number was: 201010153257.2 Chinese patent discloses a kind of image enchancing method and system, described method comprises: previous frame image is transformed to gray space by rgb space, obtain gray level image corresponding to previous frame image, calculate the average gray of described gray level image, and described average gray and default threshold value are compared, obtain gray scale comparative result; Described previous frame image is carried out to contrast stretching, and contrast stretching parameter is determined according to described gray scale comparative result; Image after contrast stretching and described previous frame image are carried out to image co-registration, obtain the image after strengthening.Utilize above-mentioned image enchancing method to process the poor image of contrast, can for user provide contrast more clear, comprise the more image of detailed information.Above-mentioned figure Enhancement Method is by the image after contrast stretching and described previous frame image are carried out to image co-registration, to reach the object that strengthens image.Yet affected by environment larger of above-mentioned image enchancing method, while causing image ash to cover as the greasy weather, utilizes said method image enchancing method can not make image return to good brightness, contrast and color and presents, and makes it to be more suitable for eye-observation.When and for example ambient light illumination is lower at dusk, gray-level reduces, and image covers to people's subjective feeling ash, utilizes contrast and the color saturation of said method lifting image limited, and the effect of image is unsatisfactory.In view of this, be necessary above-mentioned image enchancing method to be further improved.
Summary of the invention
The present invention proposes and a kind ofly based on FPGA, realize digital video image and strengthen system and method thereof, what mainly solve is that in prior art, image enchancing method is subject to the impact of environment large, the problem of image ash illiteracy, stereovision and the color saturation deficiency easily causing.
For solving the problems of the technologies described above, the technical scheme that the present invention adopts is: provide a kind of and realize based on FPGA the method that digital video image strengthens, comprise current frame image mist elimination treatment step and the anti-reflection treatment step to current frame image;
Described " the mist elimination treatment step to video image " realized by dark primary priori mist elimination algorithm, specifically comprises the steps:
S11, previous frame image is carried out to pre-service, obtain the gray level image that previous frame image is corresponding, output after aliging with original image;
S12, get grey image R GB passage minimum value as respective pixel dark primary value, with this, ask for dark primary figure, and dark primary figure is carried out to intensity profile statistics, capture vegetarian refreshments number more than the maximum gray scale of 200 as atmosphere light value;
S13, according to dark primary figure and atmosphere light value, ask for transmitance figure, and the reference value of processing transmitance figure as current frame image mist elimination;
S14, the transmitance figure asking for according to previous frame image and atmosphere light value carry out mist elimination processing to current frame image;
The current frame image that S15, output mist elimination are processed;
Described " the anti-reflection treatment step to video image " realized by image histogram equalization algorithm, specifically comprises the steps:
The pixel number of S21, each gray level of statistics previous frame image, obtains the histogram of previous frame image;
S22, setting pixel number threshold value, the part that pixel number in all gray levels is greater than to this threshold value is cumulative, then accumulation result is divided equally to all gray levels;
S23, the pixel of each gray level in amended histogram is added up;
Current frame image after S24, input mist elimination, and draw histogram-equalized image according to parabolic equation;
The current frame image of S25, output equalization processing.
In a concrete scheme, described step S11 pre-service previous frame image specifically comprises step:
The rgb video data of S111, extraction previous frame image, and rgb video data-switching is become to yuv space data;
S112, extract the gradation data Y in yuv space data, and convert gradation data Y to obtain previous frame image after rgb space data gray level image.
Wherein, in described step S13, the computing formula of the transmitance of previous frame image is as follows:
t=1-wd
max/A
Wherein, t is transmitance, and w adjusts the factor, and dmax carries out the dark primary value after day dummy section and normal region Partitioning optimization according to dark primary figure mean value, and A is the gray level of atmosphere light component.
Wherein, in described step S14, dark primary priori mist elimination formula is as follows:
J=(I-A)/t+A
Wherein, J is the image after mist elimination, I be input have a mist image, A is the gray level of atmosphere light component, t is transmitance.
Wherein, the parabolic formula in described step S23 is:
Wherein, y is the gray level after mapping, and a is maximum gray scale coefficient, and b is the accumulation result of previous frame image slices vegetarian refreshments corresponding to input picture gray level, and s is that total valid pixel of image is counted out, and n is parabolical coefficient.
Wherein, also comprise after drawing the dark primary figure of previous frame image in described step S12, ask for the step of mean value of the gray level of dark primary figure.
For solving the problems of the technologies described above, another technical solution used in the present invention is: provide a kind of and realize digital video image enhancing system based on FPGA, comprise current frame image mist elimination treating apparatus and the anti-reflection treating apparatus to current frame image;
Described mist elimination treating apparatus comprises,
Pretreatment module, for previous frame image is carried out to pre-service, obtains the gray level image corresponding with previous frame image, output after aliging with original image;
Dark primary figure asks for module, for getting grey image R GB passage minimum value as respective pixel dark primary value, with this, ask for dark primary figure, and dark primary figure carried out to intensity profile statistics, capture vegetarian refreshments number more than the maximum gray scale of 200 as atmosphere light value;
Transmitance computing module, for asking for transmitance figure according to dark primary figure and atmosphere light value, and the reference value of processing transmitance figure as current frame image mist elimination;
Mist elimination processing module, carries out mist elimination processing for transmitance figure and the atmosphere light value of asking for according to previous frame image to current frame image;
The first output module, the current frame image of processing for exporting mist elimination;
Described histogram equalization module comprises,
Statistics with histogram module, for adding up the pixel number of each gray level of previous frame image, obtains the histogram of previous frame image;
Histogram modification module, for according to setting pixel number threshold value, the part that pixel number in all gray levels is greater than to this threshold value is cumulative, then accumulation result is divided equally to all gray levels;
Histogram accumulator module, for adding up to the pixel of amended each gray level of histogram;
Para-curve conversion module, for inputting the current frame image after mist elimination, and draws histogram-equalized image according to parabolic equation;
The second output module, for exporting the current frame image of equalization processing.
In a concrete scheme, described pretreatment module also comprises,
RGB-YUV unit, for extracting the rgb video data of previous frame image, and becomes yuv space data by rgb video data-switching;
YUV-RGB unit, for extracting the gradation data Y of yuv space data, and converts gradation data Y to the gray level image that obtains previous frame image after rgb space data.
In a concrete scheme, described dark primary figure asks for the gray level mean value that module also comprises dark primary figure and asks for unit, for calculating the mean value of dark primary figure gray level.
Useful technique effect of the present invention is: be different from image enchancing method of the prior art and be subject to the impact of environment large, the problem of image ash illiteracy, stereovision and the color saturation deficiency easily causing, the invention provides a kind of method that realizes digital video image enhancing based on FPGA, it mainly comprises two parts, the one, to current frame image mist elimination treatment step, this is realized by dark primary priori mist elimination algorithm the mist elimination treatment step of video image, can realize preferably mist elimination effect; The 2nd, and the anti-reflection treatment step to current frame image, this is realized by image histogram equalization algorithm the anti-reflection treatment step of current frame image, and the ash that can realize low-light (level) covers image and changes into image effect clearly.The present invention, by adopting above-mentioned image mist elimination step and the anti-reflection step of image, can improve brightness, contrast and the color saturation of image, makes that image is more clear, penetrating and color is full.
The present invention also provides a kind of and based on FPGA, has realized digital video image and strengthen system, and this system applies said method can improve brightness, contrast and the color saturation of image, makes that image is more clear, penetrating and color is full.
Accompanying drawing explanation
Fig. 1 the present invention is based on the process flow diagram that FPGA realizes the method for digital video image enhancing;
Fig. 2 is the block diagram of image mist elimination treating apparatus in the present invention;
Fig. 3 is the block diagram of the anti-reflection treating apparatus of image in the present invention.
Embodiment
By describing technology contents of the present invention, structural attitude in detail, being realized object and effect, below in conjunction with embodiment and coordinate accompanying drawing to be explained in detail.
RGB is a kind of color standard of industry member, that RGB is the color that represents three passages of red, green, blue by variation and their stacks each other of red (R), green (G), blue (B) three Color Channels are obtained to color miscellaneous;
A kind of colour coding method (belonging to PAL) that YUV is adopted by eurovision system is the color space that PAL and SECAM simulation color television system adopt.In modern vitascan, conventionally adopt three pipe colour cameras or colored CCD video camera to carry out capture, then the colour picture signal of obtaining through color separation, obtain RGB after amplification correction respectively, through matrixer, obtain grey scale signal Y and two colour difference signal R-Y(are U again), B-Y(is V)
Refer to Fig. 1, the present embodiment is a kind of realizes based on FPGA the method that digital video image strengthens, and comprises current frame image mist elimination treatment step and the anti-reflection treatment step to current frame image;
Described " the mist elimination treatment step to video image " realized by dark primary priori mist elimination algorithm, can remove to a great extent the impact of fog on image, kept the color of image, specifically comprises the steps:
S11, previous frame image is carried out to pre-service, obtain the gray level image that previous frame image is corresponding, output after aliging with original image;
S12, get grey image R GB passage minimum value as respective pixel dark primary value, with this, ask for dark primary figure, and dark primary figure is carried out to intensity profile statistics, capture vegetarian refreshments number more than the maximum gray scale of 200 as atmosphere light value;
S13, according to dark primary figure and atmosphere light value, ask for transmitance figure, and the reference value of processing transmitance figure as current frame image mist elimination;
S14, the transmitance figure being asked for by previous frame image and atmosphere light value carry out mist elimination processing to current frame image;
The current frame image that S15, output mist elimination are processed;
Described " the anti-reflection treatment step to video image " realized by image histogram equalization algorithm, specifically comprises step:
The pixel number of S21, each gray level of statistics previous frame image, obtains the histogram of previous frame image;
S22, setting pixel number threshold value, the part that pixel number in all gray levels is greater than to this threshold value is cumulative, then accumulation result is divided equally to all gray levels;
S23, the pixel of each gray level in amended histogram is added up;
Current frame image after S24, input mist elimination, and draw histogram-equalized image according to parabolic equation;
The current frame image of S25, output equalization processing.
In a specific embodiment, described step S11 pre-service previous frame image specifically comprises step:
The rgb video data of S111, extraction previous frame image, and rgb video data-switching is become to yuv space data;
S112, extract the gradation data Y in yuv space data, and convert gradation data Y to obtain previous frame image after rgb space data gray level image.Utilize the mutual conversion between RGB and YUV to obtain the gray level image of original image, and then ask for dark primary figure, can eliminate to a great extent the impact of colored noise on dark primary figure result like this.
In a specific embodiment, in described step S13, the computing formula of the transmitance of previous frame image is as follows:
t=1-wd
max/A
Wherein, t is transmitance, and w adjusts the factor, and dmax carries out the dark primary value after day dummy section and normal region Partitioning optimization according to dark primary figure mean value, and A is the gray level of atmosphere light component.In the present invention, be to utilize the average gray value of dark primary figure and the intensity settings of dark primary priori mist elimination automatically to regulate this value, to realize the self-adaptation of effect.
In a specific embodiment, in described step S14, dark primary priori mist elimination formula is as follows:
J=(I-A)/t+A
Wherein, J is the image after mist elimination, I be input have a mist image, A is the gray level of atmosphere light component, t is transmitance.
In a specific embodiment, the parabolic formula in described step S23 is:
Wherein, y is the gray level after mapping, and a is maximum gray scale coefficient, and b is the accumulation result of previous frame image slices vegetarian refreshments corresponding to input picture gray level, and s is that total valid pixel of image is counted out, and n is parabolical coefficient.Use above-mentioned parabolic type transforming function transformation function, by adjusting coefficient, can give prominence to targetedly dark space or clear zone details.
In addition, in such scheme, by the result of calculation of previous frame image is applied on present frame, reduce the needs that carry out buffer memory one two field picture with DDR, reduced system complexity.
The present invention is different from image enchancing method of the prior art and is subject to the impact of environment large, the problem of image ash illiteracy, stereovision and the color saturation deficiency easily causing, the invention provides a kind of method that realizes digital video image enhancing based on FPGA, it mainly comprises two parts, the one, to current frame image mist elimination treatment step, this is realized by dark primary priori mist elimination algorithm the mist elimination treatment step of video image, can realize preferably mist elimination effect; The 2nd, and the anti-reflection treatment step to current frame image, this is realized by image histogram equalization algorithm the anti-reflection treatment step of current frame image, and the ash that can realize low-light (level) covers image and changes into image effect clearly.The present invention, by adopting above-mentioned image mist elimination step and the anti-reflection step of image, can improve brightness, contrast and the color saturation of image, makes that image is more clear, penetrating and color is full.
Consult Fig. 2 and Fig. 3, the present invention also provides a kind of and realizes digital video image enhancing system based on FPGA, comprises current frame image mist elimination treating apparatus and the anti-reflection treating apparatus to current frame image;
Described mist elimination treating apparatus comprises,
Pretreatment module, for previous frame image is carried out to pre-service, obtains the gray level image that previous frame image is corresponding, output after aliging with original image;
Dark primary figure asks for module, for getting grey image R GB passage minimum value as respective pixel dark primary value, with this, ask for dark primary figure, and dark primary figure carried out to intensity profile statistics, capture vegetarian refreshments number more than the maximum gray scale of 200 as atmosphere light value.
Transmitance computing module, for asking for transmitance figure according to dark primary figure and atmosphere light value, and the reference value of processing transmitance figure as current frame image mist elimination;
Mist elimination processing module, carries out mist elimination processing for transmitance figure and the atmosphere light value of asking for according to previous frame image to current frame image;
The first output module, the current frame image of processing for exporting mist elimination;
Described histogram equalization module comprises,
Statistics with histogram module, for adding up the pixel number of each gray level of previous frame image, obtains the histogram of previous frame image;
Histogram modification module, for according to setting pixel number threshold value, the part that pixel number in all gray levels is greater than to this threshold value is cumulative, then accumulation result is divided equally to all gray levels;
Histogram accumulator module, for adding up to the pixel of amended each gray level of histogram;
Para-curve conversion module, for inputting the current frame image after mist elimination, and draws histogram-equalized image according to parabolic equation;
The second output module, for exporting the current frame image of equalization processing.
In a concrete scheme, described pretreatment module also comprises,
RGB-YUV unit, for extracting the rgb video data of previous frame image, and becomes yuv space data by rgb video data-switching;
YUV-RGB unit, for extracting the gradation data Y of yuv space data, and converts gradation data Y to the gray level image that obtains previous frame image after rgb space data.
The present invention also provides a kind of and based on FPGA, has realized digital video image and strengthen system, and this system applies said method can improve brightness, contrast and the color saturation of image, makes that image is more clear, penetrating and color is full.
The invention has the beneficial effects as follows, reasonable combination dark primary priori mist elimination and two kinds of algorithms of histogram equalization, and every kind of algorithm has all carried out effective improvement on canonical algorithm basis, realized the anti-reflection and mist elimination function of good image, and on FPGA, realized the real-time processing to video.
Dark primary priori mist elimination improvement part:
1, when asking for dark primary figure, first utilize the mutual conversion between RGB and YUV to obtain the gray level image of previous frame image, and then ask for dark primary figure, can eliminate to a great extent the impact of colored noise on dark primary figure result like this;
2, when asking for atmosphere light component, with pixel number, be greater than the maximum gray scale of dark primary figure of 200 as atmosphere light component, and when differing certain numerical value, upper atmosphere light component result of calculation once and currency just upgrade this value, can avoid like this result generation saltus step, increase stability;
3, in canonical algorithm, while calculating transmitance figure, having an adjustment factor is to need artificial setting, and be to utilize the average gray value of dark primary figure and the intensity settings of dark primary priori mist elimination automatically to regulate this value in the present invention, to realize the self-adaptation of effect;
4, utilize the result of calculation of previous frame image to be applied on present frame, reduced the needs that carry out buffer memory one two field picture with DDR, reduced system complexity.
The improvement part of histogram equalization:
1, increased histogram modification module, reduced the spinoff of canonical algorithm, for example enhancing, dark space loss of detail are crossed in clear zone, have realized the controlled of effect;
2, the transforming function transformation function of canonical algorithm is changed to parabolic type transforming function transformation function, by adjusting coefficient, can give prominence to targetedly dark space or clear zone details;
3, utilize the result of calculation of previous frame image to be applied on present frame, reduced the needs that carry out buffer memory one two field picture with DDR, reduced system complexity.
The foregoing is only embodiments of the invention; not thereby limit the scope of the claims of the present invention; every equivalent structure or conversion of equivalent flow process that utilizes instructions of the present invention and accompanying drawing content to do; or be directly or indirectly used in other relevant technical fields, be all in like manner included in scope of patent protection of the present invention.
Claims (9)
1. based on FPGA, realize the method that digital video image strengthens, it is characterized in that, comprise current frame image mist elimination treatment step and the anti-reflection treatment step to current frame image;
Described " the mist elimination treatment step to current video image " realized by dark primary priori mist elimination algorithm, specifically comprises the steps:
S11, previous frame image is carried out to pre-service, obtain the gray level image that previous frame image is corresponding, output after aliging with original image;
S12, get grey image R GB passage minimum value as respective pixel dark primary value, with this, ask for dark primary figure, and dark primary figure is carried out to intensity profile statistics, capture vegetarian refreshments number more than the maximum gray scale of 200 as atmosphere light value;
S13, according to dark primary figure and atmosphere light value, ask for transmitance figure, and the reference value of processing transmitance figure as current frame image mist elimination;
S14, the transmitance figure being asked for by previous frame image and atmosphere light value carry out mist elimination processing to current frame image;
The current frame image that S15, output mist elimination are processed;
Described " the anti-reflection processing to video image " step realizes by image histogram equalization algorithm, specifically comprises step:
The pixel number of S21, each gray level of statistics previous frame image, obtains the histogram of previous frame image;
S22, setting pixel number threshold value, the part that pixel number in all gray levels is greater than to this threshold value is cumulative, then accumulation result is divided equally to all gray levels;
S23, the pixel of each gray level in amended histogram is added up;
Current frame image after S24, input mist elimination, and draw histogram-equalized image according to parabolic equation;
The current frame image of S25, output equalization processing.
2. the method that realizes digital video image enhancing based on FPGA according to claim 1, is characterized in that, described step S11 pre-service previous frame image specifically comprises step:
The rgb video data of S111, extraction previous frame image, and rgb video data-switching is become to yuv space data;
S112, extract the gradation data Y in yuv space data, and convert gradation data Y to obtain previous frame image after rgb space data gray level image.
3. the method that realizes digital video image enhancing based on FPGA according to claim 1, is characterized in that, in described step S13, the computing formula of the transmitance of previous frame image is as follows:
t=1-wd
max/A，
Wherein, t is transmitance, and w adjusts the factor, and dmax carries out the dark primary value after day dummy section and normal region Partitioning optimization according to dark primary figure mean value, and A is the gray level of atmosphere light component.
4. the method that realizes digital video image enhancing based on FPGA according to claim 1, is characterized in that, in described step S14, dark primary priori mist elimination formula is as follows:
J=(I-A)/t+A
Wherein, J is the image after mist elimination, I be input have a mist image, A is the gray level of atmosphere light component, t is transmitance.
5. the method that realizes digital video image enhancing based on FPGA according to claim 1, is characterized in that, the parabolic formula in described step S23 is:
Wherein, y is the gray level after mapping, and a is maximum gray scale coefficient, and b is the accumulation result of previous frame image slices vegetarian refreshments corresponding to input picture gray level, and s is that total valid pixel of image is counted out, and n is parabolical coefficient.
6. according to claim 1ly based on FPGA, realize the method that digital video image strengthens, it is characterized in that, also comprise after drawing the dark primary figure of previous frame image in described step S12, ask for the step of mean value of the gray level of dark primary figure.
7. based on FPGA, realize digital video image and strengthen a system, it is characterized in that, comprise current frame image mist elimination treating apparatus and the anti-reflection treating apparatus to current frame image;
Described mist elimination treating apparatus comprises,
Pretreatment module, for previous frame image is carried out to pre-service, obtains the gray level image corresponding with previous frame image, output after aliging with original image;
Dark primary figure asks for module, for getting grey image R GB passage minimum value as respective pixel dark primary value, with this, ask for dark primary figure, and dark primary figure carried out to intensity profile statistics, capture vegetarian refreshments number more than the maximum gray scale of 200 as atmosphere light value;
Transmitance computing module, for asking for transmitance figure according to dark primary figure and atmosphere light value, and the reference value of processing transmitance figure as current frame image mist elimination;
Mist elimination processing module, carries out mist elimination processing for transmitance figure and the atmosphere light value of asking for according to previous frame image to current frame image;
The first output module, the current frame image of processing for exporting mist elimination;
Described histogram equalization module comprises,
Statistics with histogram module, for adding up the pixel number of each gray level of previous frame image, obtains the histogram of previous frame image;
Histogram modification module, for according to setting pixel number threshold value, the part that pixel number in all gray levels is greater than to this threshold value is cumulative, then accumulation result is divided equally to all gray levels;
Histogram accumulator module, for adding up to the pixel of amended each gray level of histogram;
Para-curve linear transform module, for inputting the current frame image after mist elimination, and draws histogram-equalized image according to parabolic equation;
The second output module, for exporting the current frame image of equalization processing.
8. according to claim 7ly a kind ofly based on FPGA, realize digital video image and strengthen system, it is characterized in that, described pretreatment module also comprises,
RGB-YUV unit, for extracting the rgb video data of previous frame image, and becomes yuv space data by rgb video data-switching;
YUV-RGB unit, for extracting the gradation data Y of yuv space data, and converts gradation data Y to the gray level image that obtains previous frame image after rgb space data.
9. according to claim 7ly a kind ofly based on FPGA, realize digital video image and strengthen system, it is characterized in that, described dark primary figure asks for the gray level mean value that module also comprises dark primary figure and asks for unit, for calculating the mean value of dark primary figure gray level.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN104506755A (en) * | 2015-01-13 | 2015-04-08 | 武汉烽火众智数字技术有限责任公司 | Method for real-time automatic defogging of high-definition videos based on FPGA |
| CN104700107A (en) * | 2015-03-31 | 2015-06-10 | 徐晶 | Electric energy meter reading recognition apparatus based on image processing |
| CN104732497A (en) * | 2015-03-31 | 2015-06-24 | 北京交通大学 | Image defogging method, FPGA and defogging system including FPGA |
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| CN106023118A (en) * | 2016-06-13 | 2016-10-12 | 凌云光技术集团有限责任公司 | Image defogging method and realization method on FPGA |
| CN106355556A (en) * | 2015-07-21 | 2017-01-25 | 杭州海康威视数字技术股份有限公司 | Image defogging method and equipment |
| CN106846234A (en) * | 2016-12-22 | 2017-06-13 | 捷开通讯(深圳)有限公司 | A kind of image/video Enhancement Method based on FPGA, system and equipment |
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| CN109961412A (en) * | 2019-03-18 | 2019-07-02 | 浙江大华技术股份有限公司 | A kind of video frame images defogging method and equipment |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070165947A1 (en) * | 2006-01-16 | 2007-07-19 | Paik Joon-Ki | Image processing method and system using gain controllable clipped histogram equalization |
| CN103164845A (en) * | 2011-12-16 | 2013-06-19 | 中国科学院沈阳自动化研究所 | Real-time image mist elimination device and method |
-
2013
- 2013-12-26 CN CN201310732749.0A patent/CN103714520B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070165947A1 (en) * | 2006-01-16 | 2007-07-19 | Paik Joon-Ki | Image processing method and system using gain controllable clipped histogram equalization |
| CN103164845A (en) * | 2011-12-16 | 2013-06-19 | 中国科学院沈阳自动化研究所 | Real-time image mist elimination device and method |
Non-Patent Citations (4)
| Title |
|---|
| XIAOQIANG JI等: "Real-time defogging processing of aerial images", 《WIRELESS COMMUNICATIONS NETWORKING AND MOBILE COMPUTING (WICOM), 2010 6TH INTERNATIONAL CONFERENCE ON》 * |
| 元海文等: "基于FPGA的视频直方图均衡化的改进设计", 《传感器与微系统》 * |
| 高彦平等: "基于抛物线调整的直方图均衡化", 《福建电脑》 * |
| 龚亮 等: "基于FPGA的视频图像去雾系统的设计与实现", 《电视技术》 * |
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