English(EN) LaST-SR: Laplace-Inspired Steady-Transient Complex-Frequency Decomposition for Single Image Super-Resolution

新的LaST-SR方法通过复频率分解增强图像超分辨率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员推出了一种新颖的单图像超分辨率方法LaST-SR，该方法利用复频率分解技术。该技术结合了用于广泛图像上下文的全局傅里叶分支和用于详细变化的局部复频率分支。一个协作聚合模块融合了两个分支的特征，从而改进了不规则结构和精细细节的重建。实验表明，在2倍和4倍超分辨率的PSNR和SSIM方面，LaST-SR的性能优于现有方法。 AI

影响这种新方法有望在各种应用中实现更详细、结构更一致的图像重建。

排序理由该集群包含一篇详细介绍图像超分辨率新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Linhao Li, Zhaojie Pan, Langkun Chen · 2026-09-03 04:00

LaST-SR：受拉普拉斯启发的稳态瞬态复频率分解用于单图像超分辨率

arXiv:2609.02063v1 Announce Type: new Abstract: Single-image super-resolution (SISR) requires global context modeling for structurally consistent reconstruction. Fourier operators are increasingly adopted for global feature modeling. However, their periodic spectral bases constra…

本文如何被排名

报道来源 [1]

LaST-SR：受拉普拉斯启发的稳态瞬态复频率分解用于单图像超分辨率

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