论文标题
基于相邻像素的差异的单图像超分辨率重建
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels
论文作者
论文摘要
深度学习技术用于提高单像超分辨率（SISR）的性能。但是，大多数现有的基于CNN的SISR方法主要致力于建立更深层或更大的网络以提取更重要的高级功能。通常，使用目标高分辨率图像和估计图像之间的像素级损失，但是很少使用图像中像素之间的邻居关系。另一方面，根据观察结果，像素的邻居关系包含有关空间结构，局部环境和结构知识的丰富信息。基于这一事实，在本文中，我们以不同的角度利用像素的邻居关系，我们提出了相邻像素的差异，以通过从估计的图像和基地真实图像中构造图形来正规化CNN。所提出的方法在基准数据集的定量和定性评估方面优于最先进的方法。 关键词：超分辨率，卷积神经网络，深度学习
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
