Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan

TL;DR

This paper introduces AnimalHaze3k, a synthetic hazy wildlife image dataset, and IncepDehazeGan, a novel GAN architecture that significantly improves image dehazing, aiding conservation efforts.

Contribution

The paper presents a new synthetic dataset and a novel GAN-based dehazing method that outperforms existing approaches in wildlife imagery enhancement.

Findings

IncepDehazeGan achieves SSIM of 0.8914, PSNR of 20.54, and LPIPS of 0.1104.

Dehazed images improve YOLOv11 detection mAP by 112%.

The dataset and method support better wildlife monitoring in hazy conditions.

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.