Single Image Dehazing through Improved Atmospheric Light Estimation
This work addresses visibility issues in outdoor vision for smart car auxiliary transport systems, but it is incremental as it builds on existing dehazing methods.
The paper tackled the problem of inaccurate atmospheric light estimation in single image dehazing, which often misidentifies bright objects like car lights, by proposing an optimized method that uses a semi-globally adaptive filter, resulting in enhanced images with reduced noise and improved texture and edge details.
Image contrast enhancement for outdoor vision is important for smart car auxiliary transport systems. The video frames captured in poor weather conditions are often characterized by poor visibility. Most image dehazing algorithms consider to use a hard threshold assumptions or user input to estimate atmospheric light. However, the brightest pixels sometimes are objects such as car lights or streetlights, especially for smart car auxiliary transport systems. Simply using a hard threshold may cause a wrong estimation. In this paper, we propose a single optimized image dehazing method that estimates atmospheric light efficiently and removes haze through the estimation of a semi-globally adaptive filter. The enhanced images are characterized with little noise and good exposure in dark regions. The textures and edges of the processed images are also enhanced significantly.