The luminance channel of an image, Y, represents the brightness of each pixel through a linear combination of the red, green, and blue pixel values. The DnCNN network is trained to detect the residual image from the luminance of a color image. For this example, distortion appears as JPEG blocking artifacts. The residual image contains information about the image distortion. A residual image is the difference between a pristine image and a distorted copy of the image. The reference paper employs a residual learning strategy, meaning that the DnCNN network learns to estimate the residual image. However, the DnCNN architecture can also be trained to remove JPEG compression artifacts or increase image resolution. The network was primarily designed to remove noise from images. This example uses a built-in deep feed-forward convolutional neural network, called DnCNN.
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