Unveiling Clarity: Deep Learning-Based Dehazing with Reference Image Enhancement

Abstract

Single image dehazing is a challenging task that aims to remove haze from images captured in adverse weather conditions. Traditional dehazing methods often suffer from limitations such as halo artifacts, color distortion, and high computational cost. Deep learning-based techniques have recently achieved state-of-the-art results in single image dehazing, but they can still be inaccurate and computationally expensive, especially in challenging scenes with complex haze distributions.In this paper, we propose a novel approach to single image dehazing that combines deep learning with a reference image. The reference image is an image of the same scene taken without haze. By comparing the hazy image to the reference image, our deep learning model can learn to better estimate the transmission map and dehaze the image more accurately.Our approach offers a number of advantages over traditional single image dehazing methods:Improved dehazing quality: Our approach can produce dehazed images with higher quality and fewer artifacts, especially in challenging scenes with complex haze distributions.Reduced computational cost: Our approach is more efficient than traditional deep learning-based dehazing methods, making it more suitable for real-time applications.Increased generalization ability: Our approach can generalize well to new hazy images, even if they are captured in different environments or with different cameras.We evaluate our approach on a number of benchmark datasets and demonstrate that it outperforms state-of-the-art single image dehazing methods in terms of both quantitative and qualitative metrics.Overall, our proposed approach to single image dehazing with deep learning and reference image is a promising new research direction that offers significant advantages over traditional dehazing methods. We believe that our approach has the potential to be used in a variety of real-world applications, such as autonomous driving, augmented reality, and medical imaging.