A traditional image pyramid is an effective way to extract multiscale features. However, for image restoration, downsampling and upsampling lose details in the original resolution and result in an over smooth result. To overcome this defect, we propose a receptive pyramid (RP) that replaces downsampling by dilated convolution to extract multiscale features on the original resolution and make a full resolution correction. We present an RP-based convolution neural network named receptive pyramid convolutional network (RPCN) for efficient color image compression artifact reduction (CAR). Specifically, we propose residual connected convolution blocks as the baseline of RPCN. RPs work in convolution blocks to extract the hierarchical multiscale features for local feature correction. Moreover, the global feature fusion and vector correction are also introduced to further exploit the hierarchical features from the baseline. Benefiting from the RP, our RPCN achieves state-of-the-art performance with a much smaller model and much faster running speed for the CAR task.
Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction. This study proposes a scalable CNN called S-Net. Our approach effectively adjusts the network scale dynamically in a multitask system for real-time operation with little performance loss. It offers a simple and direct technique to evaluate the performance gains obtained with increasing network depth, and it is helpful for removing redundant network layers to maximize the network efficiency. We implement our architecture using the Keras framework with the TensorFlow backend on an NVIDIA K80 GPU server. We train our models on the DIV2K dataset and evaluate their performance on public benchmark datasets. To validate the generality and universality of the proposed method, we created and utilized a new dataset, called WIN143, for over-processed images evaluation. Experimental results indicate that our proposed approach outperforms other CNN-based methods and achieves state-of-the-art performance.