Interactive grayscale image colorizing method based on local linear model optimization
A local linear, grayscale image technology, applied in image data processing, 2D image generation, filling planes with attributes, etc., can solve problems such as dependence on similarity, color penetration, easy color penetration, etc.
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Embodiment 1
[0068] Such as figure 1 As shown, the interactive grayscale image colorization method based on local linear model optimization described in this embodiment includes the following six steps:
[0069] (1) Input the grayscale image to be processed, then convert the grayscale image to be processed into RGB color space, and the generated image is used as the input image;
[0070] (2) Carry out artificial line coloring to the input image in step (1), obtain the coloring image;
[0071] (3) Convert the RGB color space of the input image and the coloring image to the YUV color space respectively, obtain the luminance component Y, the chroma component U and the chroma component V of the image after conversion, and mark the luminance component of the input image at YUV as I , the shaded image has a YUV chroma component of S U and S V , S U and S V is an N×1 matrix, and N is the product of the length and width of the image;
[0072] The conversion method that described is converted...
Embodiment 2
[0139] The interactive grayscale image colorization method based on local linear model optimization described in this embodiment is different from Embodiment 1 in that in step (4), the Laplacian matting matrix is calculated according to the following formula:
[0140]
[0141] In the formula:
[0142] i, j and k are image pixel index values;
[0143] The matting Laplacian matrix L is an N×N matrix;
[0144] N is the product of the length and width of the image;
[0145] δ ij is the Kronecker function, if i and j are equal, then δ ij is 1, otherwise δ ij is 0;
[0146] mu k and are the ω centered at k in the luminance component I, respectively k The mean and variance of the pixels in the window, in this method ω k Use a 3×3 window;
[0147] |ω k |Indicates the number of pixels in the window;
[0148] ∈ is the regularization parameter;
[0149] I is the brightness component of input image in YUV in step (3);
[0150] D. t is the diffusion distance, solved by...
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