Abstract
Single image super-resolution (SISR) is a traditional image restoration problem. Given an image with low resolution (LR), the task of SISR is to find the homologous high-resolution (HR) image. As an ill-posed problem, there are works for SISR problem from different points of view. Recently, deep learning has shown its amazing performance in different image processing tasks. There are works for image super-resolution based on convolutional neural network (CNN). In this paper, we propose an adaptive residual channel attention network for image super-resolution. We first analyze the limitation of residual connection structure and propose an adaptive design for suitable feature fusion. Besides the adaptive connection, channel attention is proposed to adjust the importance distribution among different channels. A novel adaptive residual channel attention block (ARCB) is proposed in this paper with channel attention and adaptive connection. Then, a simple but effective upscale block design is proposed for different scales. We build our adaptive residual channel attention network (ARCN) with proposed ARCBs and upscale block. Experimental results show that our network could not only achieve better PSNR/SSIM performances on several testing benchmarks but also recover structural textures more effectively.
Highlights
Super-resolution (SR) is an important issue in the image restoration area. e task of single image super-resolution (SISR) is to find high-resolution (HR) images from the lowresolution (LR) images
We proposed a novel adaptive residual channel attention network named ARCN for single image superresolution (SISR) problem
Mixture factors in ARCB were learned while training, which weighted the information from two paths in blocks adaptively
Summary
Super-resolution (SR) is an important issue in the image restoration area. e task of single image super-resolution (SISR) is to find high-resolution (HR) images from the lowresolution (LR) images. E task of single image super-resolution (SISR) is to find high-resolution (HR) images from the lowresolution (LR) images Since it is an ill-posed problem, there are potential high-resolution images corresponding to an identical image with low resolution. As far as we know, SRCNN [6] is the first work using a three-layer CNN for image super-resolution. After SRCNN, Dong et al proposed FSRCNN [7] with a deeper but narrower network and achieved better performance with less time cost. VDSR [8] proposed by Kim et al used a very deep network design with global residual learning. Inspired by VDSR and residual connections, EDSR [9] proposed by Lim et al applied an enhanced deep network for SISR problem with residual blocks. Similar to MDSR, the progressive LapSRN could upscale images with different scaling factors concurrently. DRRN [12] proposed by Tai et al combined residual and recursive structures and achieved good performances
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