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 SLA/GEL 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 effectiveness of this proposed method was evaluated by training state-of-the-art denoising models on the BVI-Lowlight dataset, which features a wide range of Goalie - Trappers - Junior real noise distortions.Adding a topological term to common loss functions leads to a significant increase in the LPIPS (Learned Perceptual Image Patch Similarity) metric, with the improvement reaching up to 25%.The results indicate that the proposed loss function enables neural networks to learn noise characteristics better.
We demonstrate that they can consequently extract the topological features of noise-free images, resulting in enhanced contrast and preserved textural information.