Unsupervised Deep Image Prior‐Based Neural Networks for Single Image Super‐Resolution: Comparative Analysis and Modelling Guidelines

ABSTRACT Deep Image Prior (DIP) has been recently introduced as a method to exploit the structural priors inherent to neural networks. In the field of image processing, DIP effectively addresses various problems such as denoising, inpainting, image restoration and super‐resolution. Unlike supervised neural networks, which require large amounts of labelled data, DIP operates as a single‐image method, where prior knowledge is derived directly from the architecture of the neural network. In this work, we focus on the single‐image super‐resolution problem using DIP. Through extensive experiments for image super‐resolution, we show that the original formulation of DIP can be improved by properly modelling fidelity with multiple down‐sampling operators. Our experimental results systematically explore combinations of regularisation and fidelity terms across both hyperspectral and natural RGB image datasets, offering new guidelines for developing effective DIP‐based approaches. Code and data are available at https://github.com/capo‐urjc/dip‐sisr .