For years, Single Image Super Resolution (SISR) has been an interesting and ill-posed problem in computer vision. The traditional super-resolution (SR) imaging approaches involve interpolation, reconstruction, and learning-based methods. Interpolation methods are fast and uncomplicated to compute, but they are not so accurate and reliable. Reconstruction-based methods are better compared with interpolation methods, but they are time-consuming and the quality degrades as the scaling increases. Even though learning-based methods like Markov random chains are far better than all the previous ones, they are unable to match the performance of deep learning models for SISR. This study examines the Residual Dense Networks architecture proposed by Yhang et al. [17] and analyzes the importance of its components. By leveraging hierarchical features from original low-resolution (LR) images, this architecture achieves superior performance, with a network structure comprising four main blocks, including the residual dense block (RDB) as the core. Through investigations of each block and analyses using various loss metrics, the study evaluates the effectiveness of the architecture and compares it to other state-of-the-art models that differ in both architecture and components.

翻译：多年来，单图像超分辨率一直是计算机视觉中一个有趣且不适定的问题。传统的超分辨率成像方法包括插值法、重建法以及基于学习的方法。插值法计算快速且简单，但精度和可靠性不足；重建法虽优于插值法，但耗时且随缩放比例增大质量下降；基于学习的方法（如马尔可夫随机链）虽远超前两者，却仍无法匹配深度学习模型在单图像超分辨率中的性能。本研究考察了Yhang等人[17]提出的残差稠密网络架构，并分析了其各组件的重要性。通过利用原始低分辨率图像的层次化特征，该架构实现了卓越性能，其网络结构包含四个主要模块，其中残差稠密块（RDB）为核心。通过对各模块的探究及多种损失度量的分析，本研究评估了该架构的有效性，并将其与架构和组件均存在差异的其他先进模型进行了比较。