HINet: Half Instance Normalization Network for Image Restoration
arXiv:2105.06086
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 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.
References in corpus (10)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- Instance Normalization: The Missing Ingredient for Fast Stylization
- SGDR: Stochastic Gradient Descent with Warm Restarts
- Clearing the Skies: A deep network architecture for single-image rain removal
- Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
- Wide Activation for Efficient and Accurate Image Super-Resolution
- Variational Denoising Network: Toward Blind Noise Modeling and Removal
- Multi-Stage Progressive Image Restoration
- CycleISP: Real Image Restoration via Improved Data Synthesis