Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References
This work provides a user-friendly solution for low-light image enhancement, allowing users to customize brightness levels based on their specific application scenarios or aesthetic preferences, which is an incremental improvement over existing methods.
This paper addresses the subjectivity of low-light image enhancement by proposing a neural network that generates multiple enhancement levels based on user-selected brightness reference images. The method decomposes images into low-coupling feature components, allowing for the combination of content from low-light images and luminance from references, thereby preserving color information.
It is suggested that low-light image enhancement realizes one-to-many mapping since we have different definitions of NORMAL-light given application scenarios or users' aesthetic. However, most existing methods ignore subjectivity of the task, and simply produce one result with fixed brightness. This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference. Inspired by style transfer, our method decomposes an image into two low-coupling feature components in the latent space, which allows the concatenation feasibility of the content components from low-light images and the luminance components from reference images. In such a way, the network learns to extract scene-invariant and brightness-specific information from a set of image pairs instead of learning brightness differences. Moreover, information except for the brightness is preserved to the greatest extent to alleviate color distortion. Extensive results show strong capacity and superiority of our network against existing methods.