Semantic-aware enhancement: Integrating semantic compensation with 3-Dimensional Lookup Tables for low-light image enhancement

Abstract

In the realm of low-light image enhancement, the proliferation of deep learning techniques in recent years has led to significant improvements in image quality and clarity. Despite the progress, the conventional use of 3-Dimensional Look-Up Tables (3D LUTs) for color space discretization primarily yields a global average enhancement, which may not cater to the localized semantic nuances of images, and not only neglect the brightness details but also tends to lead to color inconsistencies. In order to solve the above problems, in this paper, our study presents an innovative semantic compensation model built on 3D LUT. The primary objective of this model is to meticulously preserve the color fidelity across various semantic regions, concurrently ensuring the retention of brightness and texture nuances inherent in images. Specifically, we introduce the Semantic Embedding Module (SEM), which meticulously infuses semantic details into the enhancement process, ensuring that the colors of the enhanced images remain true to the original content. In SEM, Feature Aggregation Block (FAB) is proposed to solve the entanglement problem of multi-level features and dynamically adjust the weights, thus helping to reconstruct images with sufficient brightness, complete details, and more realistic color representation. Furthermore, recognizing the dependency of existing methods on semantic labels for acquiring semantic maps, we propose the innovative Semantic Contrast loss. This loss function is instrumental in generating semantic maps independently of semantic labels, thus expanding their practical utility. Our contributions effectively bridge the gap between global enhancement techniques and the need for localized semantic accuracy, paving the way for more sophisticated low-light image enhancement methodologies. Our experimental evaluations, conducted on several synthetic and natural datasets, corroborate the superior performance of our method compared to state-of-the-art methods. We release our code at https://github.com/zyy317077/SAE/.