CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task

In real-world scenarios, images captured often suffer from blurring, noise, and other forms of image degradation, and due to sensor limitations, people usually can only obtain low dynamic range images. To achieve high-quality images, researchers have attempted various image restoration and enhancement operations on photographs, including denoising, deblurring, and high dynamic range imaging. However, merely performing a single type of image enhancement still cannot yield satisfactory images. In this paper, to deal with the challenge above, we propose the Composite Refinement Network (CRNet) to address this issue using multiple exposure images. By fully integrating information-rich multiple exposure inputs, CRNet can perform unified image restoration and enhancement. To improve the quality of image details, CRNet explicitly separates and strengthens high and low-frequency information through pooling layers, using specially designed Multi-Branch Blocks for effective fusion of these frequencies. To increase the receptive field and fully integrate input features, CRNet employs the High-Frequency Enhancement Module, which includes large kernel convolutions and an inverted bottleneck ConvFFN. Our model secured third place in the first track of the Bracketing Image Restoration and Enhancement Challenge, surpassing previous SOTA models in both testing metrics and visual quality.

1. Introduction

In real photography scenarios, images captured are often subject to image degradation such as blur and noise due to lighting conditions and exposure time limitations. Moreover, due to typical sensor limitations, people usually obtain low dynamic range images. To address these issues, various methods have been studied for deblurring, denoising, and HDR imaging. However, these methods often focus only on individual specific tasks, and the resulting images are still unsatisfactory. To address this issue, establishing a model that can handle the Unified Image Restoration and Enhancement Task is crucial.