عنوان مقاله فارسی: یادگیری درشت تا خوب برای وضوح تصویر تک تصویر

عنوان مقاله لاتین: Coarse-to-Fine Learning for Single-Image Super-Resolution

نویسندگان: Kaibing Zhang; Dacheng Tao; Xinbo Gao; Xuelong Li; Jie Li

تعداد صفحات: 13

سال انتشار: 2017

زبان: لاتین

Abstract:

This paper develops a coarse-to-fine framework for single-image super-resolution (SR) reconstruction. The coarse-to-fine approach achieves high-quality SR recovery based on the complementary properties of both example learning-and reconstruction-based algorithms: example learning-based SR approaches are useful for generating plausible details from external exemplars but poor at suppressing aliasing artifacts, while reconstruction-based SR methods are propitious for preserving sharp edges yet fail to generate fine details. In the coarse stage of the method, we use a set of simple yet effective mapping functions, learned via correlative neighbor regression of grouped low-resolution (LR) to high-resolution (HR) dictionary atoms, to synthesize an initial SR estimate with particularly low computational cost. In the fine stage, we devise an effective regularization term that seamlessly integrates the properties of local structural regularity, nonlocal self-similarity, and collaborative representation over relevant atoms in a learned HR dictionary, to further improve the visual quality of the initial SR estimation obtained in the coarse stage. The experimental results indicate that our method outperforms other state-learned HR dictionaryof-the-art methods for producing high-quality images despite that both the initial SR estimation and the followed enhancement are cheap to implement.