Deep Learning for Image Super-Resolution - Gowrinath Jarugula (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Image super-resolution (SR) is an important problem in computer vision that aims to increase the spatial resolution of an image and to recover fine details from an input low-resolution observation. Conventional interpolation and reconstruction-based approaches can hardly obtain high-quality reconstructions because of their lack of flexibility in modeling intricate image priors. The development of deep learning has transformed the SR into data-driven domain learning hierarchical representations and complex transformation between low-level features and high-level representations from large-scale databases. This paper comprises a review of deep learning-based SR methods, classifying them into different categories according to the network architectures (e.g., convolutional neural networks, generative adversarial networks and transformer models), training strategies, and loss functions. In this paper consider trade-offs for reconstruction accuracy and perceptual quality, we describe evaluation measures, we indicate challenges namely computational cost and needing for real-world degradation modelling and generalization over a variety of scenarios. Finally, And suggest some appealing future research directions towards more efficient, robust and practical model for practical tasks.