Deep Learning for Single Image Super-Resolution: A Brief Review

Understand

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions.

• To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance.
• In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning.
• For each category, a baseline is firstly established and several critical limitations of the baseline are summarized.