Super-Resolution

Super-Resolution (SR) refers to the task of generating high-resolution (HR) images from low-resolution (LR) input images. The goal is to recover fine details and texture information that is lost during the downsampling process. This is a challenging problem as the missing information cannot be directly inferred from the available information.

There are several approaches to SR, including interpolation-based methods, reconstruction-based methods, and learning-based methods. Interpolation-based methods involve simple operations such as bilinear or bicubic interpolation. Reconstruction-based methods involve solving an optimization problem to recover the missing information. Learning-based methods involve training a model to learn the mapping between LR and HR images.

Recently, deep learning approaches have shown promising results in SR, particularly with the use of convolutional neural networks (CNNs). These networks are able to learn complex mappings between LR and HR images, and can generate high-quality results. Some popular deep learning models for SR include SRCNN, VDSR, SRGAN, and EDSR.