Single image super-resolution based on gradient profile prior and nonlocal self-similarity feature

Yan Fang; Miaowen Shi; Xuemei Li; Yi Liu

doi:10.1117/12.2548653

14 August 2019 Single image super-resolution based on gradient profile prior and nonlocal self-similarity feature

Yan Fang, Miaowen Shi, Xuemei Li, Yi Liu

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111794P (2019) https://doi.org/10.1117/12.2548653
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

Image super-resolution has received great attention in recent years. In order to produce a high-quality HR image with minimal artifacts, we propose a new image super-resolution method. We propose a diffusion function to refine the gradient directions along the edges. Based on the neighboring gradient profiles, a GPS optimization function is devised to make the estimated sharpness more accurate. In order to break the limitations of traditional non-local self-similarity method, we propose a new non-fixed search method to search for non-local self-similarity image patches. Besides, gradient profile prior is used for suppressing the ringing artifacts effectively. A new image reconstruction framework is designed by combining gradient profile prior and non-local self-similarity prior. Finally, we propose a high-pass filter function to get the high-frequency components, which then enhance the image quality and edge details by shock filter. The experimental results demonstrate that the new algorithm surpasses the previous state-of-the-art methods, in both visual quality and PSNR /SSIM/IFC performance.

Citation Download Citation

Yan Fang, Miaowen Shi, Xuemei Li, and Yi Liu "Single image super-resolution based on gradient profile prior and nonlocal self-similarity feature", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111794P (14 August 2019); https://doi.org/10.1117/12.2548653

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