This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as well as to any other state-of-the-art methods. We were able to train networks with 8 layers in a single step and in relatively short time by combining residual learning, skip architecture, and symmetric weight initialization. We provide further insights into convolution networks for JPEG artifact reduction by evaluating three different objectives, generalization with respect to training dataset size, and generalization with respect to JPEG quality level.
Dong @cite_21 introduced super-resolution convolutional neural network (SRCNN) to deal with the ill-posed problem of super-resolution. The SRCNN is designed according the classical sparse coding methods -- the three layers of SRCNN consist of feature extraction layer, a high dimensional mapping layer, and a final reconstruction layer. The very deep CNN based super-resolution method proposed by Kim @cite_32 builds on the work of Dong @cite_21 and it shows that deep networks for super-resolution can be trained when proper guidelines are followed. They initialized networks properly and they used so-called residual learning in which the network predicts how the input image should be changed instead of predicting the desired image directly. Residual learning appears to be very important in super-resolution. The resulting 20 layers deep networks trained with adjustable gradient clipping significantly outperform previous approaches. However, it is unclear how effective residual learning would be in other image processing tasks where the networks inputs and outputs are not correlated that strongly as in super-resolution. We follow this approach in our work on JPEG reconstruction.
@cite_21 @cite_32
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We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We present a highly accurate single-image super-resolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification simonyan2015very . We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates ( @math times higher than SRCNN dong2015image ) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.