Efficient Image Dehazing Algorithms with Conventional and Deep Learning Based Techniques Using Multi-Spectral Images
| dc.contributor.author | Bhavani, Murapaka Dhana Lakshmi | |
| dc.date.accessioned | 2026-10-13T06:18:40Z | |
| dc.date.available | 2026-10-13T06:18:40Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Image haze is a common problem in photography and imaging that occurs due to particles like dust, smoke, and moisture in the atmosphere. These particles scatter light, which leads to a reduction in visibility and a degradation in image quality. This scattering effect reduces contrast and visual clarity. The impact of haze is particularly significant in real-time applications, such as autonomous driving, surveillance, and aerial photography, where the quality of visual data is crucial for performance and accuracy. In autonomous driving, for instance, clear images are essential for the vehicle’s vision systems to accurately detect and interpret the surrounding environment. Haze can obscure important details like road signs, pedestrians, and other vehicles, potentially leading to unsafe situations. Similarly, in surveillance, clear imagery is critical for the accurate identification of individuals and activities. Hazy images can hinder the effectiveness of these systems, compromising security and safety. To address these challenges, image dehazing techniques have been developed. These techniques aim to restore the clarity and contrast of hazy images, making them more useful for their intended applications. Image dehazing can be approached using conventional methods as well as deep learning methodologies. Conventional dehazing methods are often based on physical models of atmospheric scattering. However, conventional methods have limitations, particularly in handling complex and diverse haze conditions. This thesis mainly focus on the development of dehazing algorithms, both conventional and deep learning-based, has significantly advanced the field of image processing. These algorithms not only improve the visual quality of images but also enhance the performance of vision systems in various applications. By addressing issues related to low contrast and haze, these techniques contribute to safer autonomous driving, more effective surveillance, and higher-quality aerial photography. The first approach proposed in this thesis is a novel methodology for dehazing single images using multi-scale image fusion. The process involves extracting different scale images from a single hazy image, specifically a white-balanced image and a luminance-based image. These images are then fused using their corresponding weight maps. To avoid unwanted enhancements, pyramid decomposition is applied to the weight maps and input images, enhancing contrast and sharpening the image. This approach effectively removes haze, improving both color and texture information, and produces clear images from a single hazy input. A fast and efficient method is developed with an improved U-Net architecture for image dehazing, enhancing images captured under low illumination, fog, and snow conditions. The methodology involves replacing the pooling operations in the traditional U-Net with Discrete Wavelet Transform (DWT) for up and downsampling. Additionally, an attention module is introduced by fusing up and down samples to identify and recover missing low-level features in the up-samples. Another approach is introduced with a DeepNet convolutional network architecture for effective haze removal from RGB images by accurately estimating the transmission map, indicating light transmission at each pixel. The DeepNet model leverages deep learning to predict the initial transmission map, capturing essential features of haze-affected areas. This initial map is then refined using Near-Infrared (NIR) images, which provide additional, non-visible information. A multi-spectral transmission map fusion approach is employed to further enhance accuracy, combining transmission maps from both RGB and NIR images. The fusion process utilizes haze-aware weight generation, assigning appropriate weights to different regions based on their haze levels, thus leveraging the strengths of each spectral domain for a more detailed and accurate transmission map. Finally a novel image dehazing framework is presented using a Generative Adversarial Network (GAN) architecture, featuring a U-Net generator and dual discriminators. The methodology involves leveraging the GAN’s capabilities to transform hazy input images into clear, dehazed versions. The U-Net generator, renowned for its efficiency in image-to-image translation tasks, plays a central role in this process. Also, dual discriminators for two different spectral input (RGB and NIR) images. It enhances the dehazing by effectively learning and removing intricate haze patterns. The framework trains the GAN to generate high-quality, perceptually realistic images, resulting in significantly improved image quality. This advanced approach provides a robust and reliable solution for generating clear, haze-free images from hazy inputs, crucial for outdoor vision applications. The proposed image dehazing architectures were evaluated using various publicly available datasets, including HazySky, I-Haze, O-Haze, See-in-the-Dark (SID), Exclusively Dark Image Dataset (ExDARK), REalistic Single Image DEhazing (RESIDE), and RGB-NIR. Additionally, the performance of low-light image enhancement using an improved U-Net algorithm was analyzed with several real-time images captured on the campus of the National Institute of Technology Silchar. This study presents efficient and optimized approaches for image dehazing. | |
| dc.identifier.uri | http://nits.ndl.gov.in/handle/123456789/231 | |
| dc.language.iso | en | |
| dc.publisher | Department of Electronics and Communication Engineering, NITS | |
| dc.relation.ispartofseries | TH791 | |
| dc.title | Efficient Image Dehazing Algorithms with Conventional and Deep Learning Based Techniques Using Multi-Spectral Images |