🤖 AI Summary
This work addresses the limitations of existing deep learning approaches for RAW image denoising, which often neglect classical denoising priors, resulting in overly complex models with limited generalization. To overcome this, we propose the first learnable non-local module that explicitly embeds the classical non-local self-similarity prior into a neural network. Our method integrates multi-scale feature extraction, learnable matching and collaborative filtering, noise-level map conditioning, and joint training on both synthetic and real-world noise. The resulting model achieves performance comparable to state-of-the-art CNN- and Transformer-based methods across multiple benchmarks and real datasets, while significantly reducing parameter count and demonstrating strong cross-sensor generalization capability.
📝 Abstract
Being one of the oldest and most basic problems in image processing, image denoising has seen a resurgence spurred by rapid advances in deep learning. Yet, most modern denoising architectures make limited use of the technical knowledge acquired researching the classical denoisers that came before the mainstream use of neural networks, instead relying on depth and large parameter counts. This poses a challenge not only for understanding the properties of such networks, but also for deploying them on real devices which may present resource constraints and diverse noise profiles. Tackling both issues, we propose an architecture dedicated to RAW-to-RAW denoising that incorporates the interpretable structure of classical self-similarity-based denoisers into a fully learnable neural network. Our design centers on a novel nonlocal block that parallels the established pipeline of neighbor matching, collaborative filtering and aggregation popularized by nonlocal patch-based methods, operating on learned multiscale feature representations. This built-in nonlocality efficiently expands the receptive field, sufficing a single block per scale with a moderate number of neighbors to obtain high-quality results. Training the network on a curated dataset with clean real RAW data and modeled synthetic noise while conditioning it on a noise level map yields a sensor-agnostic denoiser that generalizes effectively to unseen devices. Both quantitative and visual results on benchmarks and in-the-wild photographs position our method as a practical and interpretable solution for real-world RAW denoising, achieving results competitive with state-of-the-art convolutional and transformer-based denoisers while using significantly fewer parameters. The code is available at https://github.com/MIA-UIB/nonlocal-matchfilter .