LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising

by   Zichun Wang, et al.
Beijing Institute of Technology
Baidu, Inc.

Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blind-spot networks (BSNs). For recent methods targeting real-world denoising, they either suffer from ignoring this spatial correlation, or are limited by the destruction of fine textures for under-considering the correlation. In this paper, we present a novel method called LG-BPN for self-supervised real-world denoising, which takes the spatial correlation statistic into our network design for local detail restoration, and also brings the long-range dependencies modeling ability to previously CNN-based BSN methods. First, based on the correlation statistic, we propose a densely-sampled patch-masked convolution module. By taking more neighbor pixels with low noise correlation into account, we enable a denser local receptive field, preserving more useful information for enhanced fine structure recovery. Second, we propose a dilated Transformer block to allow distant context exploitation in BSN. This global perception addresses the intrinsic deficiency of BSN, whose receptive field is constrained by the blind spot requirement, which can not be fully resolved by the previous CNN-based BSNs. These two designs enable LG-BPN to fully exploit both the detailed structure and the global interaction in a blind manner. Extensive results on real-world datasets demonstrate the superior performance of our method. https://github.com/Wang-XIaoDingdd/LGBPN


page 1

page 3

page 4

page 5

page 6

page 8


Recurrent Self-Supervised Video Denoising with Denser Receptive Field

Self-supervised video denoising has seen decent progress through the use...

Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots

Real noisy-clean pairs on a large scale are costly and difficult to obta...

Self-Supervised Image Denoising for Real-World Images with Context-aware Transformer

In recent years, the development of deep learning has been pushing image...

I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images

Although the advances of self-supervised blind denoising are significant...

Exploring Asymmetric Tunable Blind-Spots for Self-supervised Denoising in Real-World Scenarios

Self-supervised denoising has attracted widespread attention due to its ...

AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network

Blind-spot network (BSN) and its variants have made significant advances...

Transfer learning for self-supervised, blind-spot seismic denoising

Noise in seismic data arises from numerous sources and is continually ev...

Code Repositories


LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising

view repo

Please sign up or login with your details

Forgot password? Click here to reset