High-noise image denoising method based on deep convolutional network
A deep convolution and high-noise technology, applied in the fields of image processing and computer vision, to achieve the effect of solving covariate transfer, reducing boundary artifacts, and improving denoising efficiency
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[0050] like figure 1 As shown, this embodiment provides a method for denoising a high-noise image based on a deep convolutional network, and the process includes:
[0051] Step S1: selection of data set;
[0052] Step S2: Preprocessing the selected data set;
[0053] Step S3: Combining with the noise type in the image, establish a symmetrical expanded convolutional residual network;
[0054] Step S4: sending the noisy image and the corresponding clear label image into the symmetrical expanded convolutional residual network to obtain an image denoising network model;
[0055] Step S5: By solving the value of the minimized loss function, the optimal parameters of the network model are learned, and the noise image is restored by using the trained network model.
[0056] In step S1, in this embodiment, 300 images in BSDS300 are used as a training set, and Set68 is used as a test set, which contains 68 natural images.
[0057] In step S2, this embodiment uses additive Gaussian ...
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