Can be broken down into 2 main periods:

• A rather slow history with various interpolation algorithms of increasing complexity before deep neural networks

• An incredibly fast evolution since the advent of deep learning (DL)

SR history Pre-DL

Pixel-wise interpolation prior to DL

Various methods ranging from simple (e.g. nearest-neighbour, bicubic) to complex (e.g. Gaussian process regression, iterative FIR Wiener filter) algorithms

SR history Pre-DL

Nearest-neighbour interpolation

Simplest method of interpolation

Simply uses the value of the nearest pixel

Bicubic interpolation

Consists of determining the 16 coefficients \(a_{ij}\) in:

\[p(x, y) = \sum_{i=0}^3\sum_{i=0}^3 a\_{ij} x^i y^j\]

SR history with DL

Deep learning has seen a fast evolution marked by the successive emergence of various frameworks and architectures over the past 10 years

Some key network architectures and frameworks:

• CNN
• GAN
• Transformers

These have all been applied to SR

SRCNN