Wavelet-Based Topological Loss for Low-Light Image Denoising.

ABSTRACT: Despite significant advances in image denoising, most algorithms rely on supervised learning, with their performance largely dependent on the quality and diversity of training data. It is widely assumed that digital image distortions are caused by spatially invariant Additive White Gaussian Noise (AWGN). However, the analysis of real-world data suggests that this assumption is invalid. Therefore, this paper tackles image corruption by real noise, providing a framework to capture and utilise the underlying structural information of an image along with the spatial information conventionally used for deep learning tasks. We propose a novel denoising loss function that incorporates topological invariants and is informed by textural information extracted from the image wavelet domain. The effec

SUBMITTER: Malyugina A 

PROVIDER: S-EPMC11990961 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature