DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision

The goal of low-light image enhancement is to restore the color and details of the image and is of great significance for high-level visual tasks in autonomous driving. However, it is difficult to restore the lost details in the dark area by relying only on the RGB domain. In this paper we introduce frequency as a new clue into the network and propose a novel DCT-driven enhancement transformer (DEFormer). First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE). CFE calculates the curvature of each channel to represent the detail richness of different frequency bands, then we divides the frequency features, which focuses on frequency bands with richer textures. In addition, we propose a cross domain fusion (CDF) for reducing the differences between the RGB domain and the frequency domain. We also adopt DEFormer as a preprocessing in dark detection, DEFormer effectively improves the performance of the detector, bringing 2.1% and 3.4% improvement in ExDark and DARK FACE datasets on mAP respectively.

PE-YOLO: Pyramid Enhancement Network for Dark Object Detection

Current object detection models have achieved good results on many benchmark datasets, detecting objects in dark conditions remains a large challenge. To address this issue, we propose a pyramid enhanced network (PENet) and joint it with YOLOv3 to build a dark object detection framework named PE-YOLO. Firstly, PENet decomposes the image into four components of different resolutions using the Laplacian pyramid. Specifically we propose a detail processing module (DPM) to enhance the detail of images, which consists of context branch and edge branch. In addition, we propose a low-frequency enhancement filter (LEF) to capture low-frequency semantics and prevent high-frequency noise. PE-YOLO adopts an end-to-end joint training approach and only uses normal detection loss to simplify the training process. We conduct experiments on the low-light object detection dataset ExDark to demonstrate the effectiveness of ours. The results indicate that compared with other dark detectors and low-light enhancement models, PE-YOLO achieves the advanced results, achieving 78.0% in mAP and 53.6 in FPS, respectively, which can adapt to object detection under different low-light conditions. The code is available at https://github.com/XiangchenYin/PE-YOLO.