MKoIE

Multi-Knowledge-oriented Nighttime Haze Imaging Enhancer for Vision-driven Intelligent Transportation Systems

[Paper]

Abstract: 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. To 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 dehazing. 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. To 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.

📂 Datasets

The training and testing datasets include realistic single image dehazing (RESIDE) OTS and the composite degradation dataset (CDD).

📄 Checkpoint

checkpoint will be released later! Or you can train it by yourself.

⚙️ Installation

This codebase was tested with the following environment configurations:

• Ubuntu 20.04
• CUDA 11.8
• Python 3.8
• PyTorch 1.11.0 + cu113

🔥 Training

1. Please download the corresponding training datasets and put them in the folder.
2. Please run the prepare_patches.py and check the Train.h5 file.
3. Begin training our model.

python Train.py

🔥 Testing

1. Please download the corresponding testing datasets and put them in the other folder.
2. Please check the checkpoint.pth.tar file.
3. Begin testing our model.

python Test.py

Citation