Single Image Super-Resolution Reconstruction Based on Edge-Preserving with External and Internal Gradient Prior Knowledge

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• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10116
• 期：1
• 页码：191-205
• 丛书名：Computer Vision ?ACCV 2016 Workshops
• ISBN：978-3-319-54407-6
• 卷排序：10116

文摘

Single image super-resolution (SISR) reconstruction is currently a very fundamental and significant task in image processing. Instead of upscaling the image in spatial domain, we propose a novel SISR method based on edge preserving integrating the external gradient priors by deep learning method (auto-encoder network) and internal gradient priors using non-local total variation (NLTV). The gradient domain effectively reflects the high frequency details and edge information of nature image to some extent. The joint perspective exploits the complementary advantages of external and internal gradient prior knowledge for reconstructing the HR image. The experimental results demonstrate the effectiveness of our approach over several state-of-art SISR methods.