Image Processing Projects Using Deep Learning Phd Projects To address both issues simultaneously, we propose an image dehazing algorithm based on deep transfer learning and local mean adaptation. Nighttime image dehazing is a challenging task due to the scarcity of real hazy images and the domain gap between synthetic and real data. to address these challenges, we propose a novel deep learning framework that integrates contrastive and adversarial learning.
Kim Deep Dehazing Powered By Image Processing Network Cvprw 2023 Pdf Image dehazing task has been a challenge in field of computer vision, during adverse weather condition the image captured and appear to be very low quality of image due to presence of atmospheric particles, like fog, haze, etc. so it troubles in detecting object in image. In a variety of applications, including surveillance, automotive, remote sensing, and more, dehazing images using deep learning can significantly improve visibility, image quality, and performance. This paper proposes afe dehaze, an adaptive feature enhanced contrastive learning framework for image dehazing. by integrating hierarchical multi scale fusion, concentration aware contrastive constraints, and gradient guided optimization, the method effectively addresses the imbalance between color calibration and structural preservation in non. Published in: 2025 3rd international conference on disruptive technologies (icdt) article #: date of conference: 07 08 march 2025 date added to ieee xplore: 13 may 2025.
Unraveling The Power Of Deep Learning In Image Processing This paper proposes afe dehaze, an adaptive feature enhanced contrastive learning framework for image dehazing. by integrating hierarchical multi scale fusion, concentration aware contrastive constraints, and gradient guided optimization, the method effectively addresses the imbalance between color calibration and structural preservation in non. Published in: 2025 3rd international conference on disruptive technologies (icdt) article #: date of conference: 07 08 march 2025 date added to ieee xplore: 13 may 2025. In this work, we propose gusl dehaze, a green u shaped learning approach to image dehazing. our method integrates a physics based model with a green learning (gl) framework, offering a lightweight, transparent alternative to conventional deep learning techniques. Currently, image dehazing algorithms can be classified into traditional dehazing algorithms and those based on deep learning. this paper adopts a dehazing algorithm based on deep learning. This paper offers a detailed taxonomy of single image dehazing techniques using deep learning. it categorizes methods into supervised, semi supervised, and unsupervised learning, and discusses their architectures, training strategies, datasets, and performance metrics.
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