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There have been numerous image restoration methods based on deepconvolutional neural networks (CNNs). However, most of the literature on thistopic focused on the network architecture and loss functions, while lessdetailed on the training methods. Hence, some of the works are not easilyreproducible because it is required to know the hidden training skills toobtain the same results. To be specific with the training dataset, few worksdiscussed how to prepare and order the training image patches. Moreover, itrequires a high cost to capture new datasets to train a restoration network forthe real-world scene. Hence, we believe it is necessary to study thepreparation and selection of training data. In this regard, we present ananalysis of the training patches and explore the consequences of differentpatch extraction methods. Eventually, we propose a guideline for the patchextraction from given training images.

Training Patch Analysis and Mining Skills for Image Restoration Deep Neural Networks