Progressive Pruning of Light Dehaze Networks for Static Scenes - Byeongseon Park; Heekwon Lee; Yong-Kab Kim; Sungkwan Youm (Applied Sciences)
This paper introduces an progressive pruning method for Light DeHaze Networks, focusing on a static scene captured by a fixed camera environments. We develop a progressive pruning algorithm that aims to reduce computational complexity while maintaining dehazing quality within a specified threshold. Our key contributions include a fine-tuning strategy for specific scenes, channel importance analysis, and an progressive pruning approach considering layer-wise sensitivity. Our experiments demonstrate the effectiveness of our progressive pruning method. Our progressive pruning algorithm, targeting a specific PSNR(Peak Signal-to-Noise Ratio) threshold, achieved optimal results at a certain pruning ratio, significantly reducing the number of channels in the target layer while maintaining PSNR above the threshold and preserving good structural similarity, before automatically stopping when performance dropped below the target. This demonstrates the algorithm’s ability to find an optimal balance between model compression and performance maintenance. This research enables efficient deployment of high-quality dehazing algorithms in resource-constrained environments, applicable to traffic monitoring and outdoor surveillance. Our method paves the way for more accessible image dehazing systems, enhancing visibility in various real-world hazy conditions while optimizing computational resources for fixed camera setups.