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<p>Introducing adaptability in image dehazing solution increases the scope to accompany a broader category of images, leading to performance improvement. Another important aspect is to correct color imbalance. We propose a dynamically adaptive prior named the ratio-prior, capable of screening an image for colorcast. Through simulations we demonstrate that in a hazy image, the assumption of inherent colorcast in a night-time scene and none in day-time, reported in recent image dehazing methods, is not always true. Secondly, ratio-prior serves as a parameter for enhancing image visibility and saturation. The proposed image enhancement model relies on an iterative method to generate controlled exposure images which other fusion-based solutions have overlooked. We also incorporate a multi-scale fusion framework which fuses the generated multi-exposure images by exploiting image features via weight maps. The bright weight map for instance reduces glare due to light sources in an image. The proposed approach has been benchmarked with other state-of-the art methods both qualitatively and quantitatively affirming its effectiveness. The quantitative evaluations demonstrate significant gains over recent baselines. On the O-HAZE dataset, the proposed method achieves a + 14.86&#xa0;dB improvement in peak signal-to-noise ratio and a + 0.371 increase in structural similarity index measurement over multi-scale fusion, along with reduced perceptual error (LPIPS: 0.313 versus 0.517). Similarly, on the HazeRD dataset, it outperforms multi-scale fusion with a + 10.36&#xa0;dB peak signal-to-noise ratio gain and a + 0.439 structural similarity index measurement increase.</p>

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Image dehazing using multi-exposure fusion with weight maps and ratio-prior

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Avishek Kumar,
  • Rajib Kumar Jha,
  • Naveen K. Nishchal

摘要

Introducing adaptability in image dehazing solution increases the scope to accompany a broader category of images, leading to performance improvement. Another important aspect is to correct color imbalance. We propose a dynamically adaptive prior named the ratio-prior, capable of screening an image for colorcast. Through simulations we demonstrate that in a hazy image, the assumption of inherent colorcast in a night-time scene and none in day-time, reported in recent image dehazing methods, is not always true. Secondly, ratio-prior serves as a parameter for enhancing image visibility and saturation. The proposed image enhancement model relies on an iterative method to generate controlled exposure images which other fusion-based solutions have overlooked. We also incorporate a multi-scale fusion framework which fuses the generated multi-exposure images by exploiting image features via weight maps. The bright weight map for instance reduces glare due to light sources in an image. The proposed approach has been benchmarked with other state-of-the art methods both qualitatively and quantitatively affirming its effectiveness. The quantitative evaluations demonstrate significant gains over recent baselines. On the O-HAZE dataset, the proposed method achieves a + 14.86 dB improvement in peak signal-to-noise ratio and a + 0.371 increase in structural similarity index measurement over multi-scale fusion, along with reduced perceptual error (LPIPS: 0.313 versus 0.517). Similarly, on the HazeRD dataset, it outperforms multi-scale fusion with a + 10.36 dB peak signal-to-noise ratio gain and a + 0.439 structural similarity index measurement increase.