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

Abstract

The invention relates to an RGB color image processing method and system. The method comprises the following steps: converting an RGB image into a YIQ color space unit; extracting a Y component expressing the image brightness in the YIQ color space unit; carrying out rain removing processing on the Y component; combining the Y component after rain removing processing with an I component and a Q component and converting the combined YIQ into an RGB color space unit, thereby obtaining an RGB image after rain removing processing. With the RGB color image processing method and system, the RGB image is converted into the YIQ color space unit, the Y component is treated by the rain removing processing; and the Y component that is processed by rain removing is combined with the I component and the Q component to carry out conversion so as to obtain an RGB image. Therefore, rain removing processing of the single color image is realized; the image color distortion is prevented; and the using range of the rain removing algorithm of the single image is improved.

Description

RGB color image processing method and system

Technical field

The present invention relates to image processing field, particularly relate to a kind of RGB color image processing method and system.

Background technology

Rain has a great impact image imaging, can cause the fuzzy and information of image imaging to cover, and its direct result is that the sharpness of video image declines, and the digitized processing of video image is affected and hydraulic performance decline by this also can.The video image that polluted by raindrop is carried out to the further processing that repair process is conducive to image, comprise target detection, identification based on image, follow the trail of, cut apart and the raising of the technical feature such as monitoring.

Traditional rain algorithm that goes has a lot, as the video image based on methods such as degree of bias calculating, K mean cluster, Kalman filtering, dictionary learning and sparse coding, guiding filtering, interframe luminance differences removes rain algorithm.Wherein, single image goes rain algorithm kind a lot, carry out single image as the method by picture breakdown and remove rain, remove rain etc. by context aware, but, what traditional single image went to obtain after rain algorithm process is all gray level image, cannot process to ensure that image color is undistorted to coloured image.

Summary of the invention

Based on this, be necessary to remove for traditional single width the gray level image that is that rain method obtains, the problem that cannot process coloured image, provides a kind of RGB color image processing method and system, can realize coloured image is gone to rain processing, ensure that image color is undistorted.

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

RGB image is converted to YIQ color space;

Extract the Y component of the presentation video brightness in described YIQ color space;

Described Y component is gone to rain processing;

To go Y component after rain in conjunction with I and Q component, and in connection with after YIQ be converted to rgb color space, obtain the RGB image after rain.

Therein in an embodiment, describedly RGB image is converted to YIQ color space adopts matrix conversion.

Therein in an embodiment, describedly RGB image be converted to YIQ color space adopt the formula of matrix conversion to be:

$\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right]=\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right].$

Therein in an embodiment, described in connection with after YIQ be converted to rgb color space and adopt and ask for matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ Inverse matrix conversion.

Therein in an embodiment, described in connection with after the YIQ formula that is converted to rgb color space be:

$\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}1& 0.956& 0.621\\ 1& -0.272& -0.647\\ 1& -1.106& 1.703\end{array}\right]\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right].$

A kind of RGB color picture processing system, comprising:

The first modular converter, for being converted to YIQ color space by RGB image;

Extraction module, for extracting the Y component of presentation video brightness of described YIQ color space;

Go rain module, for described Y component is gone to rain processing;

The second modular converter, for going Y component after rain in conjunction with I and Q component, and in connection with after YIQ be converted to rgb color space, obtain the RGB image after rain.

Therein in an embodiment, describedly RGB image is converted to YIQ color space adopts matrix conversion.

Therein in an embodiment, describedly RGB image be converted to YIQ color space adopt the formula of matrix conversion to be:

$\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right]=\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right].$

Therein in an embodiment, described in connection with after YIQ be converted to rgb color space and adopt and ask for matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ Inverse matrix conversion.

Therein in an embodiment, described in connection with after the YIQ formula that is converted to rgb color space be:

$\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}1& 0.956& 0.621\\ 1& -0.272& -0.647\\ 1& -1.106& 1.703\end{array}\right]\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right].$

Above-mentioned RGB color image processing method and system, by RGB image is converted to YIQ color space, then only Y component is gone to rain processing, to go rain Y component after treatment and I, Q component combination, be converted to RGB image, that has realized single width coloured image goes rain processing, ensures that image color is undistorted, has improved the usable range that single image removes rain algorithm.

Brief description of the drawings

Fig. 1 is the process flow diagram of RGB color image processing method in an embodiment;

Fig. 2 is the schematic diagram of raindrop impact;

Fig. 3 is the structured flowchart of RGB color picture processing system in an embodiment.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.

Fig. 1 is the process flow diagram of RGB color image processing method in an embodiment.As shown in Figure 1, this RGB (Red-Green-Blue, R-G-B) color image processing method, comprises the following steps:

Step 102, is converted to YIQ color space by RGB image.

Concrete, YIQ is NTSC (National Television Standards Committee) standard television system.Y is to provide luminance signal, and I represents In-phase, and color is from orange to cyan, and Q represents Quadrature-phase, and color is from purple to yellow green.

Because the brightness of capped pixel is affected by raindrop not only, also can be subject to background influence.Consider that the camera exposure time is T, suppose that the time that raindrop during this period of time cover a certain pixel is τ, and τ is much smaller than camera exposure time T.The rain line brightness I of this pixel in time shutter T on image _brjointly determined by raindrop and background luminance, computing formula is suc as formula (1) and (2):

$I_{\mathrm{br}}={\∫}_o^{\mathrm{\τ}}{E}_r\mathrm{dt}+{\∫}_{\mathrm{\τ}}^T{E}_b\mathrm{dt}---\left(1\right)$

I _br＝I _b+ΔI (2)

Wherein, E _rthe instantaneous raindrop brightness while having raindrop to cover, E _bit is the instantaneous background luminance while covering without raindrop.I _bbe the background luminance that does not have raindrop to cover in the T time, Δ I is the brightness variable quantity that affected by raindrop.The brightness of rain line higher than background luminance be mainly because raindrop in imaging because the light within the scope of Wide-angle has more been converged in the effects such as mirror-reflection, internal reflection, refraction, as shown in Figure 2, mirror-reflection internal reflection refraction the brightness of rain line $L\left(\hat{n}\right)={\mathrm{RL}}_e\left(\hat{r}\right)+{\mathrm{SL}}_e\left(\hat{S}\right)+{\mathrm{PL}}_e\left(\hat{p}\right).$ Because of ruddiness, green glow close with blue light frequency, the critical angle approximately equal of raindrop to three, and the variation of light intensity directly determines that the brightness of pixel changes, the mirror-reflection of adding three is the same, therefore raindrop cause brightness variation delta R, Δ G, the also approximately equal of Δ B of pixel, belong to the chromatic characteristic of raindrop.

In the present embodiment, this is converted to YIQ color space by RGB image and adopts matrix conversion.

Concrete, this is converted to YIQ color space by RGB image and adopts the formula of matrix conversion to be:

$\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right]=\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right]---\left(3\right)$

Known according to formula (2), its rgb value of the pixel not affected by raindrop remains unchanged, and the pixel intensity being affected by raindrop can change, and RGB converts to after YIQ, the brightness of Y represent pixel, i.e. and the gray-scale value of gray level image, 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 formula (4) and (5), R _b, G _b, B _brespectively the background luminance value that red, green, blue triple channel is not affected by raindrop, from the chromatic characteristic of raindrop, Δ R, Δ G, Δ B approximately equal:

I＝0.596R _b-0.274G _b-0.322B _b (6)

Q＝0.211R _b-0.523G _b+0.312B _b (7)

Therefore the value of I and Q passage is not subject to the impact that raindrop brightness changes, and only has Y passage influenced.

Step 104, extracts the Y component of the presentation video brightness in this YIQ color space.

Step 106, goes rain processing to this Y component.

Concrete, adopt single image to go rain algorithm to go rain processing to this Y component.Single image goes rain algorithm can comprise (the Fu Y H such as Yu-Hsiang Fu, Kang L W, Lin C W, et al.Single-frame-based rain removal via image decomposition.In:Proceeding of 2011IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) .Prague, Czech:IEEE Press, 2011:1453-1456.) and (the Kang L W such as Li-Wei Kang, Lin C W, Fu Y H.Automatic single-image-based rain streaks removal via image decomposition.Image Processing, IEEE Transactions on, 2012, 21 (4): 1742-1755.) proposed to carry out single image by the method for picture breakdown and removed rain, (the Huang D A such as De-An Huang, Kang L W, Yang M C, et al.Context-aware single image rain removal.In:Proceeding of 2012IEEE International Conference on Multimedia and Expo (ICME) .Melbourne, Australia:IEEEPress, 2012:164-169.) propose to remove rain by context aware, (the George J such as Jaina George, Bhavani S, Jaya J.Certain explorations on removal of rain streaks using morphological component analysis.International Journal of Engineering Research & Technology.2013,2 (2) .) propose to use the method for morphology constituent analysis to remove rain, (the Chen D Y such as Duan-Yu Chen, Chen C C, Kang L W.Visual depth guided image rain streaks removal via sparse coding.In:Proceeding of2012International Symposium on Intelligent Signal Processing and Communications Systems.New Taipei, Taiwan:IEEE, 2012:151-156.) remove rain by guiding filtering and sparse coding.

Step 108, will go Y component after rain in conjunction with I and Q component, and in connection with after YIQ be converted to rgb color space, obtain the RGB image after rain.

In one embodiment, should in connection with after YIQ be converted to rgb color space and adopt and ask for matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ Inverse matrix change, obtain the RGB image after rain, its precision is high.

In one embodiment, should in connection with after the YIQ formula that is converted to rgb color space be:

$\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}1& 0.956& 0.621\\ 1& -0.272& -0.647\\ 1& -1.106& 1.703\end{array}\right]\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right]---\left(8\right)$

Above-mentioned RGB color image processing method, by RGB image is converted to YIQ color space, then only Y component is gone to rain processing, to go rain Y component after treatment and I, Q component combination, be converted to RGB image, that has realized single width coloured image goes rain processing, ensures that image color is undistorted, has improved the usable range that single image removes rain algorithm.

Fig. 3 is the structured flowchart of RGB color picture processing system in an embodiment.As shown in Figure 3, this RGB color picture processing system, comprises the first modular converter 320, extraction module 340, removes rain module 360 and the second modular converter 380.Wherein:

The first modular converter 320 is for being converted to YIQ color space by RGB image.

In one embodiment, this is converted to YIQ color space by RGB image and adopts matrix conversion.

This is converted to YIQ color space by RGB image and adopts the formula of matrix conversion to be:

$\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right]=\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right]---\left(3\right)$

Extraction module 340 is for extracting the Y component of presentation video brightness of this YIQ color space.

Go rain module 360 for this Y component is gone to rain processing.

The second modular converter 380 is for going Y component after rain in conjunction with I and Q component, and in connection with after YIQ be converted to rgb color space, obtain the RGB image after rain.

In one embodiment, should in connection with after YIQ be converted to rgb color space and adopt and ask for matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ Inverse matrix change.

In one embodiment, should in connection with after the YIQ formula that is converted to rgb color space be:

$\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}1& 0.956& 0.621\\ 1& -0.272& -0.647\\ 1& -1.106& 1.703\end{array}\right]\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right]---\left(8\right).$

Above-mentioned RGB color picture processing system, by RGB image is converted to YIQ color space, then only Y component is gone to rain processing, to go rain Y component after treatment and I, Q component combination, be converted to RGB image, that has realized single width coloured image goes rain processing, ensures that image color is undistorted, has improved the usable range that single image removes rain algorithm.

The above embodiment has only expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.

Claims (10)

1. a RGB color image processing method, comprises the following steps:

RGB image is converted to YIQ color space;

Extract the Y component of the presentation video brightness in described YIQ color space;

Described Y component is gone to rain processing;

To go Y component after rain in conjunction with I and Q component, and in connection with after YIQ be converted to rgb color space, obtain the RGB image after rain.

2. RGB color image processing method according to claim 1, is characterized in that, described by RGB image be converted to YIQ color space adopt matrix conversion.

3. RGB color image processing method according to claim 2, is characterized in that, describedly RGB image is converted to YIQ color space adopts the formula of matrix conversion to be:

$\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right]=\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right].$

4. RGB color image processing method according to claim 3, is characterized in that, described in connection with after YIQ be converted to rgb color space and adopt and ask for matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ Inverse matrix conversion.

5. RGB color image processing method according to claim 1, is characterized in that, described in connection with after the YIQ formula that is converted to rgb color space be:

$\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}1& 0.956& 0.621\\ 1& -0.272& -0.647\\ 1& -1.106& 1.703\end{array}\right]\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right].$

6. a RGB color picture processing system, is characterized in that, comprising:

The first modular converter, for being converted to YIQ color space by RGB image;

Extraction module, for extracting the Y component of presentation video brightness of described YIQ color space;

Go rain module, for described Y component is gone to rain processing;

The second modular converter, for going Y component after rain in conjunction with I and Q component, and in connection with after YIQ be converted to rgb color space, obtain the RGB image after rain.

7. RGB color picture processing system according to claim 6, is characterized in that, described by RGB image be converted to YIQ color space adopt matrix conversion.

8. RGB color picture processing system according to claim 7, is characterized in that, describedly RGB image is converted to YIQ color space adopts the formula of matrix conversion to be:

$\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right]=\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right].$

9. RGB color picture processing system according to claim 8, is characterized in that, described in connection with after YIQ be converted to rgb color space and adopt and ask for matrix $\left[\begin{array}{ccc}0.299& 0.587& 0.114\\ 0.596& -0.274& -0.322\\ 0.211& -0.523& 0.312\end{array}\right]$ Inverse matrix conversion.

10. RGB color picture processing system according to claim 6, is characterized in that, described in connection with after the YIQ formula that is converted to rgb color space be:

$\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}1& 0.956& 0.621\\ 1& -0.272& -0.647\\ 1& -1.106& 1.703\end{array}\right]\left[\begin{array}{c}Y\\ I\\ Q\end{array}\right].$