Concatenated Attention Neural Network for Image Restoration
arXiv:2006.11162
Abstract
In this paper, we present a general framework for low-level vision tasks including image compression artifacts reduction and image denoising. Under this framework, a novel concatenated attention neural network (CANet) is specifically designed for image restoration. The main contributions of this paper are as follows: First, by applying concise but effective concatenation and feature selection mechanism, we establish a novel connection mechanism which connect different modules in the modules stacking network. Second, both pixel-wise and channel-wise attention mechanisms are used in each module convolution layer, which promotes further extraction of more essential information in images. Lastly, we demonstrate that CANet achieves better results than previous state-of-the-art approaches with sufficient experiments in compression artifacts removing and image denoising.
References in corpus (8)
- Adam: A Method for Stochastic Optimization
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
- FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
- Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
- Multi-level Wavelet-CNN for Image Restoration
- S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction
- DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal