Multi-Task-oriented Nighttime Haze Imaging Enhancer for Vision-driven Measurement Systems

--Salient object detection (SOD) plays a critical role in intelligent transportation systems (ITS), facilitating the detection and segmentation of key visual elements in an image. However, adverse imaging conditions such as haze during the day, low light, and haze at night severely degrade image quality and hinder reliable object detection in real-world scenarios. T o address these challenges, we propose a multi-knowledge-oriented nighttime haze imaging enhancer (MKoIE), which integrates three tasks: daytime dehazing, low-light enhancement, and nighttime dehaz-ing. The MKoIE incorporates two key innovative components: First, the network employs a task-oriented node learning mechanism to handle three specific degradation types: day-time haze, low light, and night-time haze conditions, with an embedded self-attention module enhancing its performance in nighttime imaging. In addition, multi-receptive field enhancement module that efficiently extracts multi-scale features through three parallel depthwise separable convolution branches with different dilation rates, capturing comprehensive spatial information with minimal computational overhead to meet the requirements of real-time ITS deployment. T o ensure optimal image reconstruction quality and visual characteristics, we suggest a hybrid loss function. Extensive experiments on different types of weather/imaging conditions illustrate that MKoIE surpasses existing methods, enhancing the reliability, accuracy, and operational efficiency of ITS. The code is available at https://github.com/Ai-Chen-Lab/ ALIENT object detection (SOD) [1], [2] is a critical technology in vision-driven intelligent transportation systems (VITS), enabling the rapid and accurate identification of key objects from real-time traffic images, such as vehicles, pedestrians, and road infrastructure. High-quality SOD is essential for ensuring safety, optimizing traffic flow, and supporting automated decision-making in modern transportation networks.