Illuminating the darkness: A pyramid‐based approach with lightness attention and noise suppression

Abstract

Abstract Abstract Images taken in low‐light conditions often suffer from reduced contrast, amplified noise, and colour distortions. Such degradation falls short of viewer expectations and negatively impacts high‐level tasks, such as object detection. In this paper, the above problems are addressed by leveraging the properties of the LAB colour space and a multi‐scale feature pyramid. Specifically, the model first generates a light attention map and a noise map, which are proficient at manipulating the luminance channel for brightness recovery while preserving the colour information and separating noise from the image content. Moreover, enriched contextual features is progressively extracted and then the low‐light images are refined by incorporating the light attention map and the noise map as priors into a coarse‐to‐fine architecture. The enhanced results are characterized by improved contrast, vibrant colour, and noise suppression.

Keywords

• image enhancement
• image processing
• deep learning