RGBW mage demosaicing and deblurring combined method
A de-mosaic and de-blur technology, applied in image enhancement, image data processing, instruments, etc., can solve the problems of ringing phenomenon, inaccurate image blur kernel estimation, affecting the quality of image de-blurring, etc. the effect of clarity
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Embodiment 1
[0027] S1: According to the RGBW imaging format, extract y RGBW The r, g, b, and w channel pixels in the image constitute 4 single-channel images: y R 、y G 、y B 、y W , where y R 、y G 、y B 、y W The image size is y RGBW 1 / 4 of
[0028] S2: Estimate image y using blind convolution estimation method R 、y G 、y B 、y W The blur kernel: k r 、k g 、k b 、k w , such that:
[0029]
[0030]
[0031]
[0032]
[0033] where x r 、x g 、x b 、x w are respectively y R 、y G 、y B 、y W Corresponding sharp image, n r , n g , n b , n w is the image noise, the size of the blur kernel is: R*R;
[0034] S3: Create a matrix M of 2R*2R size, according to the RGBW imaging format, k r 、k g 、k b 、k w The middle element looks at the r, g, b, and w pixel values and fills them in the matrix M in turn, making M a RGBW format fuzzy kernel, that is, the 2*2 elements in M contain the r, g, b, and w channels fuzzy kernel value;
[0035] S4: Look at the matrix M in t...
Embodiment 2
[0039] The difference from Example 1 is that in step S5, the r, g, and b channel data in M' are taken as the blur kernels of the r, g, and b channel pixels respectively, and the image y RGB The r, g, and b channels are deconvoluted to obtain a clear image x RGB .
Embodiment 3
[0041] In step S1, the blind convolution proposed by Krishnan is used to estimate the blur kernel corresponding to the image, and the objective function of blind convolution to solve the blur kernel problem can be expressed as:
[0042]
[0043] Among them, k represents the blur kernel, also known as the point spread function PSF; x represents a clear image; y Indicates the blurred image obtained in S1; Represents a convolution operation; the first term Is the data fitting item, indicating the degree of matching between the blurred image and the clear image after convolution; the second item It is the quotient of the one-norm and two-norm of x, and it is the constraint item for the clear image x; the third item μ||k|| 1 Represents the prior knowledge of the total variant of the fuzzy kernel k, which is a constraint item for the fuzzy kernel k; λ and μ are the weight coefficients of the data fitting item and the constraint item; are energy conservation and non-negativ...
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