  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.
