Digital image defogging using feature fusion and deep learning

Subject Areas : Machine learning (deep learning, artificial neural networks, evolutionary algorithms, data mining, etc.)

Yasser Elmi Sola ^{1 *} , Rastgari Far Somayeh ²

1 -
2 - Islamic Azad University

Received: 2025-05-23 Accepted : 2025-09-11 Published : 2025-10-06

Keywords: Image Dehazing, Deep learning, Histogram Equalization, Convolutional Neural Network,

Abstract :

Fog usually causes severe interference in the image. Such image degradation presents a challenge to both human observers and computer vision systems. Image dehazing is the process of improving the quality of images that are affected by air scattering or water droplets. In this paper, a novel method for high-quality dehazing is proposed. The proposed algorithm for removing fog from images is based on a combination of two efficient mathematically based methods and the addition of a deep learning layer. The proposed method performs dehazing in the first phase based on adaptive histogram equalization and gamma correction. It operates in the HSV and RGB color spaces. In the second phase, a color channel-based method is used to dehazing by modeling it on the human brain, processing the brightness and saturation of pixels in a foggy image and examining the changes in concentration. In the third phase, a deep denoising network DnCNN is used to achieve a higher quality image. The DnCNN network was used with the justification that after image processing, the remaining fog follows a noisy pattern and can be considered as noise. The results of implementing and evaluating the proposed method on standard image databases showed that the proposed method, in addition to improving the quality of the results compared to previous methods, also performs better in terms of occupied memory. Another advantage of the proposed method is that it can be used for dehazing small-sized images.

References:


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