Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations
This addresses the practical need for efficient and adaptable low-light image enhancement in visual perception applications, though it appears incremental as it builds on existing neural implicit representation methods.
The paper tackles the problem of enhancing low-light images, especially for high-resolution and diverse unseen scenarios, by introducing CoLIE, which maps 2D coordinates to illumination using local context and achieves significant computational reduction and improved adaptability.
Current deep learning-based low-light image enhancement methods often struggle with high-resolution images, and fail to meet the practical demands of visual perception across diverse and unseen scenarios. In this paper, we introduce a novel approach termed CoLIE, which redefines the enhancement process through mapping the 2D coordinates of an underexposed image to its illumination component, conditioned on local context. We propose a reconstruction of enhanced-light images within the HSV space utilizing an implicit neural function combined with an embedded guided filter, thereby significantly reducing computational overhead. Moreover, we introduce a single image-based training loss function to enhance the model's adaptability to various scenes, further enhancing its practical applicability. Through rigorous evaluations, we analyze the properties of our proposed framework, demonstrating its superiority in both image quality and scene adaptability. Furthermore, our evaluation extends to applications in downstream tasks within low-light scenarios, underscoring the practical utility of CoLIE. The source code is available at https://github.com/ctom2/colie.