CN104112290A - RGB color image processing method and system - Google Patents

Abstract

The invention relates to a RGB color image processing method and system. The method comprises the steps of: converting an RGB image into a YIQ color space; extracting a Y component representing image brightness in the YIQ color space; carrying out derain processing on the Y component; combining the Y component after deraining with I and Q components, and convert the combined YIQ to RGB color space to obtain the RGB image after rain removal. The above-mentioned RGB color image processing method and system, by converting the RGB image into the YIQ color space, and then only performing rain removal processing on the Y component, combining the Y component after the rain removal processing with the I and Q components, and converting it into an RGB image, realizes The rain-removing processing of a single color image is realized, the color of the image is not distorted, and the application range of the rain-removing algorithm of a single image is improved.

Description

RGB color image processing method and system

technical field

The invention relates to the field of image processing, in particular to an RGB color image processing method and system.

Background technique

Rain has a great impact on image imaging, which will cause image blur and information coverage. The direct result is that the clarity of video images will decrease, and the digital processing of video images will also be affected by this and the performance will decrease. Repairing the video image polluted by raindrops is beneficial to the further processing of the image, including the improvement of the technical performance of image-based target detection, recognition, tracking, segmentation and monitoring.

There are many traditional rain removal algorithms, such as video image rain removal algorithms based on skewness calculation, K-means clustering, Kalman filter, dictionary learning and sparse coding, guided filtering, and brightness difference between frames. Among them, there are many types of single image deraining algorithms, such as deraining a single image through image decomposition, deraining through scene perception, etc. However, the traditional single image deraining algorithm is all grayscale images. , the color image cannot be processed to ensure that the color of the image is not distorted.

Contents of the invention

Based on this, it is necessary to provide a RGB color image processing method and system for the problem that the traditional single-image deraining method obtains grayscale images and cannot process color images, which can realize deraining processing of color images , to ensure that the image color is not distorted.

A kind of RGB color image processing method, comprises the following steps:

Convert RGB image to YIQ color space;

Extracting the Y component representing image brightness in the YIQ color space;

performing rain removal processing on the Y component;

Combine the I and Q components of the Y component after rain removal, and convert the combined YIQ to RGB color space to obtain the RGB image after rain removal.

In one of the embodiments, the conversion of the RGB image into the YIQ color space adopts matrix conversion.

In one of the embodiments, the formula for converting the RGB image into the YIQ color space using matrix conversion is:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.299</span>}& \mathrm{<span\; class="notranslate">\; 0.587</span>}& \mathrm{<span\; class="notranslate">\; 0.114</span>}\\ \mathrm{<span\; class="notranslate">\; 0.596</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 274</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.322</span>}\\ \mathrm{<span\; class="notranslate">\; 0.211</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.523</span>}& \mathrm{<span\; class="notranslate">\; 0.312</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; .</span>$

In one of the embodiments, the conversion of the combined YIQ to RGB color space adopts the calculation matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ The inverse matrix transformation of .

In one of the embodiments, the formula for converting the combined YIQ to RGB color space is:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0.956</span>}& \mathrm{<span\; class="notranslate">\; 0.621</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 272</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 647</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1.106</span>}& \mathrm{<span\; class="notranslate">\; 1.703</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; .</span>$

A RGB color image processing system, comprising:

The first conversion module is used to convert the RGB image into the YIQ color space;

An extraction module, configured to extract a Y component representing image brightness in the YIQ color space;

A deraining module, configured to perform deraining processing on the Y component;

The second conversion module is used to combine the Y component after rain removal with I and Q components, and convert the combined YIQ into RGB color space to obtain the RGB image after rain removal.

In one of the embodiments, the conversion of the RGB image into the YIQ color space adopts matrix conversion.

In one of the embodiments, the formula for converting the RGB image into the YIQ color space using matrix conversion is:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.299</span>}& \mathrm{<span\; class="notranslate">\; 0.587</span>}& \mathrm{<span\; class="notranslate">\; 0.114</span>}\\ \mathrm{<span\; class="notranslate">\; 0.596</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 274</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.322</span>}\\ \mathrm{<span\; class="notranslate">\; 0.211</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.523</span>}& \mathrm{<span\; class="notranslate">\; 0.312</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; .</span>$

In one of the embodiments, the conversion of the combined YIQ to RGB color space adopts the calculation matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ The inverse matrix transformation of .

In one of the embodiments, the formula for converting the combined YIQ to RGB color space is:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0.956</span>}& \mathrm{<span\; class="notranslate">\; 0.621</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 272</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 647</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1.106</span>}& \mathrm{<span\; class="notranslate">\; 1.703</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; .</span>$

The above-mentioned RGB color image processing method and system, by converting the RGB image into the YIQ color space, and then only performing rain removal processing on the Y component, combining the Y component after the rain removal processing with the I and Q components, and converting it into an RGB image, realizes The rain-removing processing of a single color image is realized, the color of the image is not distorted, and the application range of the rain-removing algorithm of a single image is improved.

Description of drawings

Fig. 1 is the flowchart of RGB color image processing method in an embodiment;

Figure 2 is a schematic diagram of the impact of raindrops;

Fig. 3 is a structural block diagram of an RGB color image processing system in an embodiment.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

FIG. 1 is a flow chart of an RGB color image processing method in an embodiment. As shown in Figure 1, this RGB (Red-Green-Blue, red-green-blue) color image processing method comprises the following steps:

Step 102, convert the RGB image into YIQ color space.

Specifically, YIQ is an NTSC (National Television Standards Committee) television system standard. Y is to provide brightness signal, I stands for In-phase, the color is from orange to cyan, Q stands for Quadrature-phase, the color is from purple to yellow-green.

Because the brightness of covered pixels is not only affected by raindrops, but also by the background. Considering that the camera exposure time is T, it is assumed that the time for raindrops to cover a certain pixel during this period is τ, and τ is much smaller than the camera exposure time T. The rain line brightness I _br of the pixel on the image within the exposure time T is determined by the raindrops and the background brightness, and the calculation formulas are as follows: (1) and (2):

${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; br</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; o</span>}}^{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

I _br =I _b +ΔI (2)

Among them, _Er is the instantaneous raindrop brightness when there is raindrop coverage, and _Eb is the instantaneous background brightness when there is no raindrop coverage. I _b is the background brightness without raindrop coverage in T time, and ΔI is the brightness change amount affected by raindrops in T time. The brightness of the rain line is higher than the background brightness mainly because the raindrops gather light in a wider field of view due to specular reflection, internal reflection, refraction, etc. when imaging, as shown in Figure 2, the specular reflection internal reflection refraction rain line brightness $L\left(\widehat{\mathrm{no}}\right)={\mathrm{RL}}_e\left(\hat{r}\right)+{\mathrm{SL}}_e\left(\hat{S}\right)+{\mathrm{PL}}_e\left(\hat{p}\right).$ Because the frequencies of red light, green light, and blue light are similar, the critical angles of raindrops to the three are approximately equal, and the change in light intensity directly determines the brightness change of the pixel. In addition, the specular reflection of the three is the same, so the raindrops cause the brightness of the pixel to change. The variation ΔR, ΔG, and ΔB are also approximately equal, which belongs to the color characteristics of raindrops.

In this embodiment, matrix conversion is used to convert the RGB image into the YIQ color space.

Specifically, the formula for converting an RGB image into a YIQ color space using matrix conversion is:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.299</span>}& \mathrm{<span\; class="notranslate">\; 0.587</span>}& \mathrm{<span\; class="notranslate">\; 0.114</span>}\\ \mathrm{<span\; class="notranslate">\; 0.596</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 274</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.322</span>}\\ \mathrm{<span\; class="notranslate">\; 0.211</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.523</span>}& \mathrm{<span\; class="notranslate">\; 0.312</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

According to formula (2), the RGB values of pixels not affected by raindrops remain unchanged, while the brightness of pixels affected by raindrops will change. After RGB is converted into YIQ, Y represents the brightness of the pixel, that is, the gray value of the grayscale image , while I and Q are:

I=0.596(R _b +ΔR)-0.274(G _b +ΔG)-0.322(B _b +ΔB) (4)

Q＝0.211(R _b +ΔR)-0.523(G _b +ΔG)+0.312(B _b +ΔB) (5)

In formulas (4) and (5), R _b , G _b , and B _b are the background brightness values of the red, green, and blue channels that are not affected by raindrops, respectively. According to the color characteristics of raindrops, ΔR, ΔG, and ΔB are approximately equal ,but:

I=0.596R _b -0.274G _b -0.322B _b (6)

Q＝0.211R _b -0.523G _b +0.312B _b (7)

Therefore, the values of the I and Q channels are not affected by changes in the brightness of raindrops, and only the Y channel is affected.

Step 104, extracting the Y component representing the brightness of the image in the YIQ color space.

Step 106, perform rain removal processing on the Y component.

Specifically, a single image deraining algorithm is used to perform deraining processing on the Y component. Single image rain removal algorithm can include Yu-Hsiang Fu et al. (Fu Y H, Kang L W, Lin C W, et al.Single-frame-based rain removal via image decomposition.In: Proceeding of2011IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague, Czech: IEEE Press, 2011: 1453-1456.) and Li-Wei Kang et al. (KangL W, Lin C W, Fu Y H. Automatic single-image-based rain streaks removal via imagedecomposition .Image Processing, IEEE Transactions on, 2012,21(4):1742-1755.) proposed a single image deraining method by image decomposition; De-An Huang et al. (Huang D A, Kang L W, Yang M C ,et al.Context-aware single image rain removal.In:Proceeding of2012IEEE International Conference on Multimedia and Expo(ICME).Melbourne,Australia:IEEEPress,2012:164-169.) proposed to remove rain through context awareness; Jaina George et al. (George J, Bhavani S, Jaya J. Certain explorations on removal of rain streaks using morphological component analysis. International Journal of Engineering Research & Technology. 2013, 2(2).) proposed to use the method of morphological component analysis to remove rain; Duan- Yu Chen et al. (Chen D Y, Chen C C, Kang L W. Visual depth guided image rain streaks removal via sparse coding. In: Proceeding of20 12 International Symposium on Intelligent Signal Processing and Communications Systems. New Taipei, Taiwan: IEEE, 2012: 151-156.) Rain removal by guided filtering and sparse coding.

Step 108, combine the I and Q components of the Y component after rain removal, and convert the combined YIQ into RGB color space to obtain the RGB image after rain removal.

In one embodiment, the conversion of the combined YIQ to RGB color space adopts the calculation matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ The inverse matrix is converted to get the RGB image after rain removal, which has high precision.

In one embodiment, the formula for converting the combined YIQ to RGB color space is:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0.956</span>}& \mathrm{<span\; class="notranslate">\; 0.621</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 272</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 647</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1.106</span>}& \mathrm{<span\; class="notranslate">\; 1.703</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

The above-mentioned RGB color image processing method converts the RGB image into the YIQ color space, then only performs rain removal processing on the Y component, and combines the Y component after the rain removal processing with the I and Q components, and converts it into an RGB image. The deraining process of a color image ensures that the color of the image is not distorted, and improves the application range of the single image deraining algorithm.

Fig. 3 is a structural block diagram of an RGB color image processing system in an embodiment. As shown in FIG. 3 , the RGB color image processing system includes a first conversion module 320 , an extraction module 340 , a rain removal module 360 and a second conversion module 380 . in:

The first conversion module 320 is used to convert the RGB image into YIQ color space.

In one embodiment, the conversion of RGB image into YIQ color space adopts matrix conversion.

The formula for converting an RGB image to a YIQ color space using matrix conversion is:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.299</span>}& \mathrm{<span\; class="notranslate">\; 0.587</span>}& \mathrm{<span\; class="notranslate">\; 0.114</span>}\\ \mathrm{<span\; class="notranslate">\; 0.596</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 274</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.322</span>}\\ \mathrm{<span\; class="notranslate">\; 0.211</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.523</span>}& \mathrm{<span\; class="notranslate">\; 0.312</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

The extraction module 340 is used for extracting the Y component representing the brightness of the image in the YIQ color space.

The deraining module 360 is used for deraining the Y component.

The second conversion module 380 is used for combining the Y component after deraining with I and Q components, and converting the combined YIQ into RGB color space to obtain the RGB image after deraining.

In one embodiment, the conversion of the combined YIQ to RGB color space adopts the calculation matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ The inverse matrix is transformed.

In one embodiment, the formula for converting the combined YIQ to RGB color space is:

$\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0.956</span>}& \mathrm{<span\; class="notranslate">\; 0.621</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 272</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 647</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1.106</span>}& \mathrm{<span\; class="notranslate">\; 1.703</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

The above-mentioned RGB color image processing system converts the RGB image into the YIQ color space, and then only performs rain removal processing on the Y component, and combines the Y component after the rain removal processing with the I and Q components, and converts it into an RGB image. The deraining process of a color image ensures that the color of the image is not distorted, and improves the application range of the single image deraining algorithm.

The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but should not be construed as limiting the patent scope of the present invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be based on the appended claims.

Claims (10)

1. A RGB color image processing method, comprising the following steps: Convert RGB image to YIQ color space; Extracting the Y component representing image brightness in the YIQ color space; performing rain removal processing on the Y component; Combine the I and Q components of the Y component after rain removal, and convert the combined YIQ to RGB color space to obtain the RGB image after rain removal. 2. RGB color image processing method according to claim 1 is characterized in that, described RGB image is converted into YIQ color space and adopts matrix conversion. 3. RGB color image processing method according to claim 2, is characterized in that, described RGB image is converted into YIQ color space and adopts the formula of matrix conversion as: $\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.299</span>}& \mathrm{<span\; class="notranslate">\; 0.587</span>}& \mathrm{<span\; class="notranslate">\; 0.114</span>}\\ \mathrm{<span\; class="notranslate">\; 0.596</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 274</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.322</span>}\\ \mathrm{<span\; class="notranslate">\; 0.211</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.523</span>}& \mathrm{<span\; class="notranslate">\; 0.312</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; .</span>$ 4. RGB color image processing method according to claim 3, is characterized in that, described YIQ after combining is converted into RGB color space and adopts to obtain matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ The inverse matrix transformation of . 5. RGB color image processing method according to claim 1, is characterized in that, the formula that described YIQ after combining is converted into RGB color space is: $\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0.956</span>}& \mathrm{<span\; class="notranslate">\; 0.621</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 272</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 647</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1.106</span>}& \mathrm{<span\; class="notranslate">\; 1.703</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; .</span>$ 6. A RGB color image processing system, characterized in that, comprising: The first conversion module is used to convert the RGB image into the YIQ color space; An extraction module, configured to extract a Y component representing image brightness in the YIQ color space; A deraining module, configured to perform deraining processing on the Y component; The second conversion module is used to combine the Y component after rain removal with I and Q components, and convert the combined YIQ into RGB color space to obtain the RGB image after rain removal. 7. RGB color image processing system according to claim 6, is characterized in that, described RGB image is converted into YIQ color space and adopts matrix transformation. 8. RGB color image processing system according to claim 7, is characterized in that, described RGB image is converted into YIQ color space and adopts the formula of matrix conversion as: $\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 0.299</span>}& \mathrm{<span\; class="notranslate">\; 0.587</span>}& \mathrm{<span\; class="notranslate">\; 0.114</span>}\\ \mathrm{<span\; class="notranslate">\; 0.596</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 274</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.322</span>}\\ \mathrm{<span\; class="notranslate">\; 0.211</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.523</span>}& \mathrm{<span\; class="notranslate">\; 0.312</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; .</span>$ 9. RGB color image processing system according to claim 8, is characterized in that, described YIQ after combining is converted into RGB color space and adopts to obtain matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ The inverse matrix transformation of . 10. RGB color image processing system according to claim 6, is characterized in that, the formula that the described YIQ after combining is converted into RGB color space is: $\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; G</span>}\\ \mathrm{<span\; class="notranslate">\; B</span>}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}\mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; 0.956</span>}& \mathrm{<span\; class="notranslate">\; 0.621</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 272</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; 647</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1.106</span>}& \mathrm{<span\; class="notranslate">\; 1.703</span>}\end{array}\right]\left[\begin{array}{c}\mathrm{<span\; class="notranslate">\; Y</span>}\\ \mathrm{<span\; class="notranslate">\; I</span>}\\ \mathrm{<span\; class="notranslate">\; Q</span>}\end{array}\right]<span\; class="notranslate">\; .</span>$