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

Kangzhen Yang, Tao Hu, Kexin Dai, Genggeng Chen, Yu Cao, Wei Dong, Peng Wu, Yanning Zhang, Qingsen Yan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6086-6096

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.