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TIENet: task-oriented image enhancement network for degraded object detection.
- Source :
- Signal, Image & Video Processing; Feb2024, Vol. 18 Issue 1, p1-8, 8p
- Publication Year :
- 2024
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18631703
- Volume :
- 18
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Signal, Image & Video Processing
- Publication Type :
- Academic Journal
- Accession number :
- 175023484
- Full Text :
- https://doi.org/10.1007/s11760-023-02695-9