基于小波的渐进式超分辨率模型

Junyi He, Hongyang Zhou, Yan Ma
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引用次数: 0

摘要

基于深度学习的超分辨率(SR)方法在许多方面都取得了显著的成绩。然而,现有的大多数方法都是针对单个特定规模构建的,很少有多规模、轻量级和短运行时间的方法。为此,我们提出了一种基于小波的渐进式超分辨方法。WPSR通过对不同小波系数进行顺序重构,分别处理低频和高频信息,并可在多个尺度上依次生成多幅SR图像。实验表明,我们的WPSR可以在多个尺度上优于现有的最先进的方法,特别是对于×8和×16尺度因子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Wavelet-based Progressive Super-Resolution Model
Deep-learning based Super-Resolution (SR) methods have achieved remarkable performance in many aspects. However, most existing methods are constructed for a single specific scale, and few are multi-scale, lightweight, and have a short running time. To this end, we propose a wavelet-based progressive super-resolution method (WPSR). WPSR could process low and high-frequency information separately by reconstructing different wavelet coefficients in order and could generate multiple SR images at multiple scales sequentially. Experiments demonstrate that our WPSR could outperform existing state-of-the-art methods at multiple scales, especially for ×8 and ×16 scale factors.
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