Zero-Shot Blind-spot Image Denoising via Implicit Neural Sampling
The blind-spot principle has been a widely used tool in zero-shot image denoising but faces challenges with real-world noise that exhibits strong local correlations. Existing methods focus on reducing noise correlation, which also weaken the pixel correlations needed for accurately estimating missing pixels. In this paper, we first present a rigorous analysis of how noise correlation and pixel correlation impact the statistical risk of a linear blind-spot denoiser. We then propose using an implicit neural representation to resample noisy pixels, effectively reducing noise correlation while preserving the essential pixel correlations for successful blind-spot denoising. Extensive experiments show our method surpasses existing zero-shot denoising techniques on real-world noisy images.
Code (1)
Tasks
DenoisingImage DenoisingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Domino Denoise: An Accurate Blind Zero-Shot Denoiser using Domino Tilings
Because noise can interfere with downstream analysis, image denoising has come to occupy an important place in the image processing toolbox. The most accurate state-of-the-art denoisers typically train on a representativ…
DenoisingImage DenoisingZero-shot Blind Image Denoising via Implicit Neural Representations
Recent denoising algorithms based on the "blind-spot" strategy show impressive blind image denoising performances, without utilizing any external dataset. While the methods excel in recovering highly contaminated images,…
DenoisingImage DenoisingInductive BiasPseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World Denoising
Real-world image denoising remains a challenge task. This paper studies self-supervised image denoising, requiring only noisy images captured in a single shot. We revamping the blind-spot technique by leveraging the tran…
DenoisingImage DenoisingBlind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots
Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile, supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised denoisers, which learn only from single no…
DenoisingImage DenoisingMasked and Shuffled Blind Spot Denoising for Real-World Images
We introduce a novel approach to single image denoising based on the Blind Spot Denoising principle, which we call MAsked and SHuffled Blind Spot Denoising (MASH). We focus on the case of correlated noise, which often pl…
DenoisingImage Denoising