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.

翻译：在真实场景中，采集的图像常受到模糊、噪声等多种退化影响，且受传感器限制，通常只能获得低动态范围图像。为获取高质量图像，研究人员尝试对照片进行多种图像复原与增强操作，包括去噪、去模糊和高动态范围成像。然而，仅执行单一类型的图像增强仍无法生成令人满意的图像。为应对这一挑战，本文提出复合精炼网络（CRNet），利用多重曝光图像解决该问题。通过充分整合信息丰富的多重曝光输入，CRNet能够执行统一的图像复原与增强任务。为提升图像细节质量，CRNet通过池化层显式分离并增强高低频信息，并采用专门设计的多分支模块对这些频率进行有效融合。为增大感受野并充分整合输入特征，CRNet采用包含大核卷积和倒瓶颈ConvFFN的高频增强模块。本模型在Bracketing图像复原与增强挑战赛的第一赛道中获得第三名，在测试指标和视觉质量上均超越了先前的SOTA模型。