Scholarlink

<p>Haze is one of the most common challenges faced by outdoor imaging systems. Hazy images are characterized by low contrast and a white overlay, which make it difficult to produce high-quality results. To address this issue, researchers have developed various haze removal methods aimed at achieving superior image enhancement, efficient processing, and cost-effectiveness. This paper introduces an enhanced Multi-scale Guided Filtering (MGF) technique for dehazing. The proposed technique incorporates several key components: a fast dehazing method, homomorphic processing, and Contrast Limited Adaptive Histogram Equalization (CLAHE) as preprocessing steps, followed by a fuzzy logic approach to enhance contrast. The core of the technique is the MGF dehazing method, which is supported by fast dehazing and fuzzy logic proessing as enhancement strategies to reduce noise and improve the dynamic range of images. The MGF dehazing process gets the transmission map and atmospheric light at the coarsest level, and then guided filtering is employed iteratively to produce a smoothed transmission map, resulting in dehazed images free from artifacts. The haze model is applied to obtain the final dehazed image. This technique effectively combines two traditional approaches—fast dehazing and MGF dehazing—into a unified framework. The performance of the proposed technique is evaluated using visible and Near Infrared (NIR) video frames, as well as real hazy images. To demonstrate its efficacy, its results are compared with those of the standalone MGF dehazing method. Additionally, a comparative analysis is conducted with other dehazing techniques using both visible and NIR frames. Evaluation metrics include both referenced and non-referenced quantitative measures such as Peak Signal-to-Noise Ratio (PSNR), correlation (to measure similarity between hazy and dehazed images), entropy (quantifying the amount of information), Feature Similarity Index (FSIM), Chronic Feature Similarity index (FSIC), Structural Similarity index (SSIM), edge intensity, Feature-similarity and Alignment-based Distortion Estimation (FADE), Natural Image Quality Evaluator (NIQE), and average gradient. The results demonstrate that the proposed technique outperforms existing techniques across all metrics. Histograms and spectral entropy analysis of dehazed frames further confirm effectiveness of the proposed technique in enhancing video quality. Notably, the inclusion of fast dehazing and fuzzy logic processing significantly improves the quality of NIR frames. The proposed MGF-based technique achieves PSNR enhancement percentages of 44.33% and 215.69% for visible and NIR images, respectively.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

High quality dehazed image and video based on enhanced multi-scale guided filtering dehazing technique

  • 期刊论文

  • 发表日期：

    2025年4月28日

查看全文
  • Abeer Ayoub,
  • Walid El-Shafai,
  • Fathi E. Abd El-Samie,
  • Ehab K. I. Hamad,
  • El-Sayed M. El-Rabaie

摘要

Haze is one of the most common challenges faced by outdoor imaging systems. Hazy images are characterized by low contrast and a white overlay, which make it difficult to produce high-quality results. To address this issue, researchers have developed various haze removal methods aimed at achieving superior image enhancement, efficient processing, and cost-effectiveness. This paper introduces an enhanced Multi-scale Guided Filtering (MGF) technique for dehazing. The proposed technique incorporates several key components: a fast dehazing method, homomorphic processing, and Contrast Limited Adaptive Histogram Equalization (CLAHE) as preprocessing steps, followed by a fuzzy logic approach to enhance contrast. The core of the technique is the MGF dehazing method, which is supported by fast dehazing and fuzzy logic proessing as enhancement strategies to reduce noise and improve the dynamic range of images. The MGF dehazing process gets the transmission map and atmospheric light at the coarsest level, and then guided filtering is employed iteratively to produce a smoothed transmission map, resulting in dehazed images free from artifacts. The haze model is applied to obtain the final dehazed image. This technique effectively combines two traditional approaches—fast dehazing and MGF dehazing—into a unified framework. The performance of the proposed technique is evaluated using visible and Near Infrared (NIR) video frames, as well as real hazy images. To demonstrate its efficacy, its results are compared with those of the standalone MGF dehazing method. Additionally, a comparative analysis is conducted with other dehazing techniques using both visible and NIR frames. Evaluation metrics include both referenced and non-referenced quantitative measures such as Peak Signal-to-Noise Ratio (PSNR), correlation (to measure similarity between hazy and dehazed images), entropy (quantifying the amount of information), Feature Similarity Index (FSIM), Chronic Feature Similarity index (FSIC), Structural Similarity index (SSIM), edge intensity, Feature-similarity and Alignment-based Distortion Estimation (FADE), Natural Image Quality Evaluator (NIQE), and average gradient. The results demonstrate that the proposed technique outperforms existing techniques across all metrics. Histograms and spectral entropy analysis of dehazed frames further confirm effectiveness of the proposed technique in enhancing video quality. Notably, the inclusion of fast dehazing and fuzzy logic processing significantly improves the quality of NIR frames. The proposed MGF-based technique achieves PSNR enhancement percentages of 44.33% and 215.69% for visible and NIR images, respectively.