Simultaneously Achieving Performance and Efficiency in Low-light RAW Enhancement Architectures

Design and validate a deep learning architecture for low-light RAW image enhancement that simultaneously achieves strong restoration performance and high computational efficiency, with particular emphasis on suitability for resource-constrained devices by minimizing parameter count and FLOPs without sacrificing image quality.

Background

Low-light RAW image enhancement targets denoising and reconstruction directly in the RAW domain, which often involves high-resolution inputs and severe noise. While prior works such as Restormer, DNF, and RetinexRawMamba have advanced performance or efficiency individually, balancing both remains difficult—especially for single-image inference at full resolution.

This challenge is critical for deployment on mobile and edge devices where computation and memory budgets are limited. The paper motivates a rethinking of architectural design to harness complementary modules (Transformer-style channel attention for large-scale features and Mamba-based state-space modeling for small-scale features) to address this gap, underscoring that the general problem of achieving both strong quality and efficiency remains unresolved.