ReCo-Diff: Explore Retinex-Based Condition Strategy in Diffusion Model for Low-Light Image Enhancement

Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. In this study, we propose ReCo-Diff, a novel approach that incorporates Retinex-based prior as an additional pre-processing condition to regulate the generating capabilities of the diffusion model. ReCo-Diff first leverages a pre-trained decomposition network to produce initial reflectance and illumination maps of the low-light image. Then, an adjustment network is introduced to suppress the noise in the reflectance map and brighten the illumination map, thus forming the learned Retinex-based condition. The condition is integrated into a refinement network, implementing Retinex-based conditional modules that offer sufficient guidance at both feature- and image-levels. By treating Retinex theory as a condition, ReCo-Diff presents a unique perspective for establishing an LLIE-specific diffusion model. Extensive experiments validate the rationality and superiority of our ReCo-Diff approach. The code will be made publicly available.

Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement

Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement.

Breaking Free from Fusion Rule: A Fully Semantic-driven Infrared and Visible Image Fusion

Infrared and visible image fusion plays a vital role in the field of computer vision. Previous approaches make efforts to design various fusion rules in the loss functions. However, these experimental designed fusion rules make the methods more and more complex. Besides, most of them only focus on boosting the visual effects, thus showing unsatisfactory performance for the follow-up high-level vision tasks. To address these challenges, in this letter, we develop a semantic-level fusion network to sufficiently utilize the semantic guidance, emancipating the experimental designed fusion rules. In addition, to achieve a better semantic understanding of the feature fusion process, a fusion block based on the transformer is presented in a multi-scale manner. Moreover, we devise a regularization loss function, together with a training strategy, to fully use semantic guidance from the high-level vision tasks. Compared with state-of-the-art methods, our method does not depend on the hand-crafted fusion loss function. Still, it achieves superior performance on visual quality along with the follow-up high-level vision tasks.