https://www.selleckchem.com/pr....oducts/glafenine.htm
In general, image restoration involves mapping from low-quality images to their high-quality counterparts. Such optimal mapping is usually nonlinear and learnable by machine learning. Recently, deep convolutional neural networks have proven promising for such learning processing. It is desirable for an image processing network to support well with three vital tasks, namely 1) super-resolution; 2) denoising; and 3) deblocking. It is commonly recognized that these tasks have strong correlations, which enable us to design a general frame
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