Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan
This work provides a dataset and method to improve wildlife image dehazing for conservation applications, but it is incremental as it applies known GAN techniques with inception blocks to a new domain.
The authors introduce AnimalHaze3k, a synthetic hazy wildlife dataset, and IncepDehazeGan, a GAN-based dehazing architecture, achieving state-of-the-art performance (SSIM: 0.8914, PSNR: 20.54, LPIPS: 0.1104) with 6.27% higher SSIM and 10.2% better PSNR than competitors. Dehazed images improved YOLOv11 detection mAP by 112% and IoU by 67%.
Atmospheric haze significantly degrades wildlife imagery, impeding computer vision applications critical for conservation, such as animal detection, tracking, and behavior analysis. To address this challenge, we introduce AnimalHaze3k a synthetic dataset comprising of 3,477 hazy images generated from 1,159 clear wildlife photographs through a physics-based pipeline. Our novel IncepDehazeGan architecture combines inception blocks with residual skip connections in a GAN framework, achieving state-of-the-art performance (SSIM: 0.8914, PSNR: 20.54, and LPIPS: 0.1104), delivering 6.27% higher SSIM and 10.2% better PSNR than competing approaches. When applied to downstream detection tasks, dehazed images improved YOLOv11 detection mAP by 112% and IoU by 67%. These advances can provide ecologists with reliable tools for population monitoring and surveillance in challenging environmental conditions, demonstrating significant potential for enhancing wildlife conservation efforts through robust visual analytics.