Low-illumination image enhancement method based on conditional re-enhancement network
A technology of network enhancement and image enhancement, applied in image enhancement, image analysis, image data processing and other directions, can solve the problem of inability to deal with low contrast, low brightness and noise and color distortion at the same time
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specific Embodiment approach 1
[0063] Specific implementation mode one: combine figure 1 To describe this embodiment,
[0064] A low-light image enhancement method based on a conditional re-enhancement network, comprising the following steps:
[0065] Step 1. Design a conditional re-enhancement network based on deep learning. The conditional re-enhancement network can enhance the input low-light image S and output the final enhanced image E;
[0066] Input the low-light image to be enhanced into the conditional re-enhancement network;
[0067] The input of the conditional re-enhancement network is the low-illumination image S and its maximum channel image S max and its expected maximum channel image S expect_max , S is a matrix of M*N*3, M is the number of rows, N is the number of columns, 3 is {r, g, b} three color channels, S max Obtained by taking the maximum value of the three color channels, it is a matrix of M*N*1, in the test phase, S expect_max Any image enhancement method such as histogram equ...
specific Embodiment approach 2
[0078] Specific implementation mode two: combination figure 2 To describe this embodiment,
[0079] A low-illuminance image enhancement method based on a conditional re-enhancement network described in this embodiment, the conditional re-enhancement network is specifically as follows:
[0080] The input is input to the first convolutional layer and the second convolutional layer respectively, and the first convolutional layer and the second convolutional layer are respectively 9*9 convolutional layer and 3*3 convolutional layer;
[0081] The first convolutional layer is connected to the third convolutional unit, and the third convolutional unit is connected to a ReLU layer after the convolutional layer of 3*3;
[0082] The third convolution unit is connected to the fourth convolution unit, the fourth convolution unit is connected to the fifth convolution unit, the fifth convolution unit is connected to the sixth convolution unit, the fourth convolution unit, the fifth convol...
specific Embodiment approach 3
[0091]A low-illuminance image enhancement method based on a conditional re-enhancement network described in this embodiment, the specific process of the third step includes the following steps:
[0092] Step 31. Extract the maximum value channel image S of the low-illumination image S max :
[0093]
[0094] Among them, S max (i, j) is the maximum channel image S max The i-th row and the j-th column element; max represents the maximum value operation; c is r, g, b, corresponding to the three color channels of red, green and blue in the rgb color space, S c (i,j) is the i-th row and j-th column element of a certain channel of the low-illuminance image S in the rgb color space;
[0095] Step 32: Extract the maximum value channel image H of the normal illumination image H max :
[0096]
[0097] Among them, H max (i, j) is the maximum channel image H max The i-th row and the j-th column element; max represents the maximum value operation; c is r, g, b, corresponding ...
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