Degradation-Equivariant Representations for Robust Feature Detection and Description in Low-Light Environments

Keypoint detection and matching have garnered significant attention, yet remain challenging in low-light environments. Most current studies follow an enhance-then-detect pipeline, which consists of independent enhancers and detectors. While the enhancer focuses on improving the visual quality of low-light images to satisfy human perception standards, the detector prioritizes detection accuracy for machine vision tasks. The unaligned optimization objectives of the enhancer and detector overlook the gap between human and machine vision and lead to sub-optimal performance in low-light keypoint detection. To tackle this problem, a joint enhance-and-detect pipeline is proposed to unify the optimization objectives of enhancement and detection by regarding the improvement of keypoint detection accuracy with enhanced features under machine vision standards. Specifically, we propose a low-light keypoint detection network named DeRFeat, which learns a degradation-equivariant representation between normal and dark domains using AutoEncoding transformation and domain descriptor similarity constraints to indirectly enhance the features from the encoder in the training stage. Then, DeRFeat guides the shared encoder to obtain the degradation-equivariant representations from dark images in the inference stage. With the dark degradation predictions, the encoder is capable of generating equivariant representations between normal and dark domains. The proposed domain descriptor similarity module further aids the encoder in mitigating the impact of dark degradation factors, enabling local descriptors to acquire undisturbed representations. Moreover, a coarse-to-fine point selection strategy is proposed to provide reliable prior keypoints for a globally optimal descriptor construction. Experimental results on four benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art methods under varying low-light conditions.