Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels

• URL: http://arxiv.org/abs/2212.13730v1
• Date: Wed, 28 Dec 2022 07:30:07 GMT
• Title: Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels
• Authors: Huipeng Zheng, Lukman Hakim, Takio Kurita, Junichi Miyao
• Abstract summary: The deep learning technique was used to increase the performance of single image super-resolution (SISR) In this paper, we propose the differences of neighboring pixels to regularize the CNN by constructing a graph from the estimated image and the ground-truth image. The proposed method outperforms the state-of-the-art methods in terms of quantitative and qualitative evaluation of the benchmark datasets.
• Score: 3.257500143434429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The deep learning technique was used to increase the performance of single image super-resolution (SISR). However, most existing CNN-based SISR approaches primarily focus on establishing deeper or larger networks to extract more significant high-level features. Usually, the pixel-level loss between the target high-resolution image and the estimated image is used, but the neighbor relations between pixels in the image are seldom used. On the other hand, according to observations, a pixel's neighbor relationship contains rich information about the spatial structure, local context, and structural knowledge. Based on this fact, in this paper, we utilize pixel's neighbor relationships in a different perspective, and we propose the differences of neighboring pixels to regularize the CNN by constructing a graph from the estimated image and the ground-truth image. The proposed method outperforms the state-of-the-art methods in terms of quantitative and qualitative evaluation of the benchmark datasets. Keywords: Super-resolution, Convolutional Neural Networks, Deep Learning

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