Abstract
In this paper, we propose an independent neural network for single image super-resolution by residual recovery. The network is inspired by the observation that there still exists image residuals between the low-resolution image and the downsampled high-resolution output obtained by a previously proposed super-resolution network. Based on this observation, we design a simple but effective deep convolutional neural network to train the mapping between the image residuals and the corresponding ground-truth residuals. Furthermore, we combine the high-resolution output generated by the previous super-resolution network and the high-resolution residual output by the proposed neural network to yield the final high-resolution image. Extensive experiments on simulated natural images and real time-of-flight (ToF) images demonstrate the effectiveness of the proposed method from the aspects of visual and quantitative performance.
Highlights
The main goal of single image super-resolution (SR) is to recover a high-resolution (HR) image from one lowresolution (LR) image while keeping clear image details
We propose the method based on an observation that there still exist visible image residuals between the LR image and the downsampled HR output generated by a previous SR method that even could be a state-of-the-art approach
Since our independent deep convolutional neural netowork (IDCNN) method is based on the PnP method, it is trained on the DIV2K dataset
Summary
The main goal of single image super-resolution (SR) is to recover a high-resolution (HR) image from one lowresolution (LR) image while keeping clear image details. How to propose an accurate and fast SR approach to increase image resolution is quite crucial, which is the main task and challenge in this work. From the perspective of methodology, existing single image SR approaches can be divided into three categories: 1) interpolation-based method; 2) statistics-based method; 3) learning-based method. The learning-based method could be roughly divided into two parts. One is the dictionary-based learning method, and the other is the deep learning-based method. The proposed method in the paper belongs to the category of the deep learning-based method
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