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.
Abstract (translated)
自监督的真实世界图像去噪仍然是一个基本的挑战，这源于解耦空间结构化噪声与保持高频细节之间的对抗性权衡。现有的盲点网络（BSN）方法依赖于像素混洗下采样（PD）来解相关噪声，但激进的下采样会破坏精细结构，而温和的下采样则无法去除相关的噪声。为了应对这一问题，我们引入了跨尺度预测（NSP），这是一种新颖的自监督范式，将噪声解相关与细节保持分离开来。在NSP中，构建了跨尺度训练对，BSN以低分辨率、完全去噪的子图像为输入，预测保留精细细节的高分辨率目标。作为副产品，NSP自然支持无需重新训练或修改即可提高含噪图像的超分辨率。广泛的实验表明，NSP在真实世界的基准测试中实现了最先进的自监督去噪性能，并显著缓解了长期以来噪声解相关与细节保持之间的冲突。
URL
https://arxiv.org/abs/2512.21038
