Segmentation-based image defogging using modified dark channel prior

• Published in: EURASIP journal on image and video processing 2020-02, Vol.2020 (1), p.1-14, Article 6
• Main Authors: Sabir, Aneela, Khurshid, Khawar, Salman, Ahmad
• Format: Article
• Language: English
• Subjects: Algorithms; Biometrics; Computer vision; Dark channel; Defogging; Density; Engineering; Fog; Guided image filter; Image acquisition; Image classification; Image contrast; Image enhancement; Image filters; Image Processing and Computer Vision; Image quality; Image segmentation; Image transmission; Object recognition; Pattern Recognition; Quality assessment; Segmentation; Signal to noise ratio; Signal,Image and Speech Processing; Sky; Visibility; Visual effects; Weather
• ISSN: 1687-5281; 1687-5176; 1687-5281
• DOI: 10.1186/s13640-020-0493-9

Image acquisition under bad weather conditions is prone to yield image with low contrast, faded color, and overall poor visibility. Different computer vision applications including surveillance, object classification, tracking, and recognition get effected due to degraded hazy images. Dehazing can significantly improve contrast, balance luminance, correct distortion, remove unwanted visual effects/ and therefore enhance the image quality. As a result, image defogging is imperative pre-processing step in computer vision applications. Previously, dark channel prior-based algorithms have proven promising results over the available techniques. In this paper, we have proposed a modified dark channel prior that uses fog density and guided image-filtering technique to estimate and refine transmission map, respectively. Guided image filter speeds up the refinement of transmission map, hence reduces the overall computational complexity of algorithm. We have also incorporated segmentation of the foggy image into sky and non-sky regions, after which, the modified dark channel prior and atmospheric light is computed for each segment. Then, the average value of atmospheric light for each segment is used to estimate transmission map. We have performed quantitative and subjective comparison for effective evaluation of our proposed algorithm against the current state-of-the-art algorithms on natural and synthetic images. Different quality metrics, such as saturation, mean square error, fog density, peak signal to noise ratio, structural similarity index metric, dehazing algorithm index (DHQI), full-reference image quality assessmen (FR-IQA), and naturalness of dehazed images have shown the proposed algorithm to be better than existing techniques.