Layer Decomposition and Morphological Reconstruction for Task-Oriented Infrared Image Enhancement
This addresses the challenge of enhancing infrared image contrast without amplifying noise for autonomous driving systems, but it appears incremental as it builds on existing enhancement techniques.
The paper tackles the problem of low contrast in infrared images for autonomous driving by proposing a layer decomposition and morphological reconstruction method, which improves image quality for object detection and semantic segmentation tasks and outperforms state-of-the-art methods.
Infrared image helps improve the perception capabilities of autonomous driving in complex weather conditions such as fog, rain, and low light. However, infrared image often suffers from low contrast, especially in non-heat-emitting targets like bicycles, which significantly affects the performance of downstream high-level vision tasks. Furthermore, achieving contrast enhancement without amplifying noise and losing important information remains a challenge. To address these challenges, we propose a task-oriented infrared image enhancement method. Our approach consists of two key components: layer decomposition and saliency information extraction. First, we design an layer decomposition method for infrared images, which enhances scene details while preserving dark region features, providing more features for subsequent saliency information extraction. Then, we propose a morphological reconstruction-based saliency extraction method that effectively extracts and enhances target information without amplifying noise. Our method improves the image quality for object detection and semantic segmentation tasks. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods.