  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.
