Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan

• URL: http://arxiv.org/abs/2604.16284v1
• Date: Fri, 17 Apr 2026 17:46:32 GMT
• Title: Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan
• Abstract summary: AnimalHaze3k is a synthetic hazy dataset of 3,477 images generated from 1,159 clear wildlife photographs.<n>Our novel IncepDehazeGan architecture combines inception blocks with residual skip connections in a GAN framework.<n>When applied to downstream detection tasks, dehazed images improved YOLOv11 detection mAP by 112% and IoU by 67%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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