  In this paper, we explore the role of Instance Normalization in low-level
vision tasks. Specifically, we present a novel block: Half Instance
Normalization Block (HIN Block), to boost the performance of image restoration
networks. Based on HIN Block, we design a simple and powerful multi-stage
network named HINet, which consists of two subnetworks. With the help of HIN
Block, HINet surpasses the state-of-the-art (SOTA) on various image restoration
tasks. For image denoising, we exceed it 0.11dB and 0.28 dB in PSNR on SIDD
dataset, with only 7.5% and 30% of its multiplier-accumulator operations
(MACs), 6.8 times and 2.9 times speedup respectively. For image deblurring, we
get comparable performance with 22.5% of its MACs and 3.3 times speedup on REDS
and GoPro datasets. For image deraining, we exceed it by 0.3 dB in PSNR on the
average result of multiple datasets with 1.4 times speedup. With HINet, we won
1st place on the NTIRE 2021 Image Deblurring Challenge - Track2. JPEG
Artifacts, with a PSNR of 29.70. The code is available at
https://github.com/megvii-model/HINet.
