Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan
TLDR
This paper introduces AnimalHaze3k, a synthetic dataset, and IncepDehazeGan, a novel architecture, to enhance hazy wildlife imagery.
Key contributions
- Created AnimalHaze3k, a synthetic dataset of 3,477 hazy wildlife images for dehazing research.
- Developed IncepDehazeGan, a novel GAN combining inception blocks and residual skip connections.
- Achieved state-of-the-art dehazing performance (SSIM: 0.8914, PSNR: 20.54) on wildlife imagery.
- Improved YOLOv11 detection mAP by 112% and IoU by 67% on dehazed images for downstream tasks.
Why it matters
Atmospheric haze significantly degrades wildlife imagery, impeding crucial conservation efforts. This work provides robust tools for ecologists, enabling reliable animal detection and tracking in challenging environmental conditions. It offers significant potential for enhancing wildlife conservation through improved visual analytics.
Original Abstract
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.
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