Next-Scale Prediction: A Self-Supervised Approach for Real-World Image Denoising

• URL: http://arxiv.org/abs/2512.21038v1
• Date: Wed, 24 Dec 2025 08:06:17 GMT
• Title: Next-Scale Prediction: A Self-Supervised Approach for Real-World Image Denoising
• Authors: Yiwen Shan, Haiyu Zhao, Peng Hu, Xi Peng, Yuanbiao Gou,
• Abstract summary: Next-Scale Prediction (NSP) is a novel self-supervised paradigm that decouples noise decorrelation from detail preservation.<n>As a by-product, NSP naturally supports super-resolution of noisy images without retraining or modification.
• Score: 34.459296950807854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised real-world image denoising remains a fundamental challenge, arising from the antagonistic trade-off between decorrelating spatially structured noise and preserving high-frequency details. Existing blind-spot network (BSN) methods rely on pixel-shuffle downsampling (PD) to decorrelate noise, but aggressive downsampling fragments fine structures, while milder downsampling fails to remove correlated noise. To address this, we introduce Next-Scale Prediction (NSP), a novel self-supervised paradigm that decouples noise decorrelation from detail preservation. NSP constructs cross-scale training pairs, where BSN takes low-resolution, fully decorrelated sub-images as input to predict high-resolution targets that retain fine details. As a by-product, NSP naturally supports super-resolution of noisy images without retraining or modification. Extensive experiments demonstrate that NSP achieves state-of-the-art self-supervised denoising performance on real-world benchmarks, significantly alleviating the long-standing conflict between noise decorrelation and detail preservation.

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