HINet: Half Instance Normalization Network for Image Restoration

TLDR

The study investigates how Instance Normalization can improve low‑level vision tasks by introducing a novel Half Instance Normalization Block. The authors build a two‑subnetwork, multi‑stage HINet architecture that incorporates the HIN Block. HINet outperforms state‑of‑the‑art methods on image denoising, deblurring, and deraining, achieving up to 0.28 dB PSNR gains and up to 3.3× speedups while winning the NTIRE 2021 Image Deblurring Challenge Track 2.

Abstract

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× and 2.9× speedup respectively. For image deblurring, we get comparable performance with 22.5% of its MACs and 3.3× 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× speedup. With HINet, we won the 1st place on the NTIRE 2021 Image Deblurring Challenge - Track2. JPEG Artifacts, with a PSNR of 29.70.