文献类型：期刊文献

英文题名：WaveDiffUR: A Wavelet-Domain Diffusion Model for Ultraresolution in Remote Sensing

作者：Shi, Yue[1];Han, Liangxiu[1];Han, Lianghao[2];Dancey, Darren[1];Zhang, Xueqin[3]

机构：[1]Manchester Metropolitan Univ, Dept Comp & Math, Manchester M1 5GD, England;[2]Brunel Univ, Dept Comp Sci, London UB8 3PH, England;[3]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2025

卷号：63

外文期刊名：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

收录：;EI(收录号：20254019265377);WOS:【SCI-EXPANDED(收录号:WOS:001589913700027)】；

基金：This work was supported in part by the Biotechnology andBiological Sciences Research Council (BBSRC) under Grant BB/Y513763/1and in part by the Engineering and Physical Sciences Research Council(EPSRC) under Grant EP/X013707/1.

语种：英文

外文关键词：Diffusion model; multiscale generative artifical intelligence (AI); remote sensing image super resolution (SR); stochastic differential equation (SDE); ultraresolution (UR); wavelet transformation; Diffusion model; multiscale generative artifical intelligence (AI); remote sensing image super resolution (SR); stochastic differential equation (SDE); ultraresolution (UR); wavelet transformation

摘要：Deep learning (DL) has significantly advanced super-resolution (SR), a technique that enhances low-quality images by reconstructing fine details. However, most DL-based SR methods struggle at high magnification levels (e.g., x4 or higher) due to dramatically increased ill-posedness. To overcome this, we define high-magnification SR as an ultraresolution (UR) problem and introduce WaveDiffUR, a novel wavelet-domain diffusion model (DM) designed for extreme-scale image reconstruction. WaveDiffUR decomposes the UR process into sequential steps, first restoring low-frequency wavelet details for global consistency and then refining high-frequency components for sharper textures. By integrating pretrained SR models as modular components, it reduces ill-posedness and ensures adaptability across different applications. Unlike existing SR approaches, which struggle with fixed boundary conditions at extreme magnifications, WaveDiffUR incorporates the cross-scale pyramid (CSP) constraint, an adaptive framework that dynamically refines low- and high-frequency wavelet details to maintain consistency and high fidelity. Extensive experiments demonstrate that WaveDiffUR with CSP notably enhances spatial accuracy and consistently generates high-frequency details with remarkable fidelity during the SR process. Evaluations are conducted across two benchmark evaluation datasets and four additional independent datasets. The empirical results reveal that, as magnification scales from x 8 to x 128, WaveDiffUR achieves an average degradation rate in peak signal-to-noise ratio (PSNR), natural image quality evaluator (NIQE), and spectral reconstruction error (SRE) of only 19.1%-the best performance among all benchmarked models-while consistently delivering sharper images characterized by superior spatial fidelity. By enabling scalable, high-fidelity ultraresolution, WaveDiffUR opens new possibilities for remote sensing applications, including environmental monitoring, urban planning, disaster response, and precision agriculture.