LIMPID: A Lightweight Image-to-Curve MaPpIng moDel for Extremely Low-Light Image Enhancement

Abstract

Low-light conditions severely degrade the captured image quality with few details, while deep learning approaches are trending towards low-light image enhancement (LLIE) due to their superior performance. However, few methods face the challenges of lower dynamic range and greater noise from extremely low-light directly. Existing methods for extremely low-light enhancement are end-to-end, requiring RAW data as input. Meanwhile, they often lack the potential for real-time mobile deployment owing to the high model complexity. In this paper, we introduce the image-to-curve transformation to ELLIE for the first time and present a Lightweight Image-to-curve MaPpIng moDel for ELLIE (LIMPID). Compared with existing image-to-curve mapping methods, the proposed module is constructed for a wider dynamic range according to the light scattering model. Furthermore, we propose a new pyramid fusion strategy based on Laplacian and Gaussian. This strategy attempts to achieve dynamic fusion of multi-scale images via learnable fusion weight parameters. Specifically, LIMPID consists of a low-resolution dense CNN network stream and a full-resolution guidance stream. First, the curve generation and refinement are achieved in the low-resolution stream constructed on a light scattering model. Then, the curves are up-sampled to full resolution via bilateral grid cells. Finally, the enhanced result is obtained through dynamically adapted multi-scale pyramid fusion. Experimental results show that our method is competitive with existing state-of-the-art methods in terms of performance.