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Single-image super resolution using evolutionary sparse coding technique

机译：使用进化稀疏编码技术的单图像超分辨率

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摘要

Sparse coding (SC) has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. SC applications have been explored in many fields such as image super resolution (SR), image-feature extraction, image reconstruction, and segmentation. In most of these applications, learning-based SC has provided an excellent image quality. SC involves two steps: dictionary construction and searching the dictionary using quadratic programming. This study focuses on the searching step and a new adaptive variation of genetic algorithm is proposed to search and find the optimum closest match in the dictionary. Also, inspired by the proposed evolutionary SC (ESC), a single-image SR algorithm is proposed. A sparse representation for each patch of the low-resolution input image is obtained by ESC and it is used to generate the high-resolution output image. Experimental results show that the proposed ESC-based method would lead to a better SR image quality.
机译：稀疏编码（SC）最近已成为信号和图像处理中广泛使用的工具。适当选择的过完整字典中元素的稀疏线性组合可以表示许多信号斑块。 SC应用已在许多领域进行了探索，例如图像超分辨率（SR），图像特征提取，图像重建和分割。在大多数这些应用中，基于学习的SC提供了出色的图像质量。 SC包括两个步骤：字典构建和使用二次编程搜索字典。本研究着重于搜索步骤，提出了一种新的遗传算法自适应变异算法，用于在字典中搜索和找到最佳的最接近匹配。此外，受提出的进化SC（ESC）的启发，提出了单图像SR算法。通过ESC获得低分辨率输入图像每个斑块的稀疏表示，并将其用于生成高分辨率输出图像。实验结果表明，所提出的基于ESC的方法将产生更好的SR图像质量。

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