TIENet: task-oriented image enhancement network for degraded object detection

• Published in: Signal, image and video processing image and video processing, 2024-02, Vol.18 (1), p.1-8
• Main Authors: Wang, Yudong, Guo, Jichang, Wang, Ruining, He, Wanru, Li, Chongyi
• Format: Article
• Language: English
• Subjects: Algorithms; Blurring; Computer Imaging; Computer Science; Datasets; Detectors; Fast Fourier transformations; Fourier transforms; Image contrast; Image degradation; Image enhancement; Image Processing and Computer Vision; Image quality; Multimedia Information Systems; Object recognition; Original Paper; Pattern Recognition and Graphics; Perception; Performance degradation; Signal,Image and Speech Processing; Vision
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-023-02695-9

Degraded images often suffer from low contrast, color deviations, and blurring details, which significantly affect the performance of detectors. Many previous works have attempted to obtain high-quality images based on human perception using image enhancement algorithms. However, these enhancement algorithms usually suppress the performance of degraded object detection. In this paper, we propose a task-oriented image enhancement network (TIENet) to directly improve degraded object detection’s performance by enhancing the degraded images. Unlike common human perception-based image-to-image methods, TIENet is a zero-reference enhancement network, which obtains a detection-favorable structure image that is added to the original degraded image. In addition, this paper presents a fast Fourier transform-based structure loss for the enhancement task. With the new loss, our TIENet enables the structure image obtained to enhance more useful detection-favorable structural information and suppress irrelevant information. Extensive experiments and comprehensive evaluations on underwater (URPC2020) and foggy (RTTS) datasets show that our proposed framework can achieve 0.5–1.6% AP absolute improvements on classic detectors, including Faster R-CNN, RetinaNet, FCOS, ATSS, PAA, and TOOD. Besides, our method also generalizes well to the PASCAL VOC dataset, which can achieve 0.2–0.7% gains. We expect this study can draw more attention to high-level task-oriented degraded image enhancement. The code and pre-trained models are available at https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/tienet .