首页|Study Results from HeNan Polytechnic University Update Understanding of Intellig ent Systems (Joint Dual-teacher Distillation and Unsupervised Fusion for Unpaire d Real-world Image Dehazing)
Study Results from HeNan Polytechnic University Update Understanding of Intellig ent Systems (Joint Dual-teacher Distillation and Unsupervised Fusion for Unpaire d Real-world Image Dehazing)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning - In telligent Systems have been presented. According to news reporting out of Jiaozu o, People's Republic of China, by NewsRx editors, research stated, "Existing lea rning-based dehazing algorithms struggle to deal with real world hazy images for lack of paired clean data. Moreover, most dehazing methods require significant computation and memory." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from HeNan Polytechnic U niversity, "To address the above problems, we propose a joint dual-teacher knowl edge distillation and unsupervised fusion framework for single image dehazing in this paper. First, considering the complex degradation factors in real-world ha zy images, two synthetic-to-real dehazing networks are explored to generate two preliminary dehazing results with the heterogeneous distillation strategy. Secon d, to get more qualified ground truth, an unsupervised adversarial fusion networ k is proposed to refine the preliminary outputs of teachers with unpaired clean images. In particular, the unpaired clean images are enhanced to deal with the d im artifacts. Furthermore, to alleviate the structure distortion in the unsuperv ised adversarial training, we constructed an intermediate image to constrain the output of the fusion network. Finally, considering the memory storage and compu tation overhead, an end-to-end lightweight student network is trained to learn t he mapping from the original hazy image to the output of the fusion network."
JiaozuoPeople's Republic of ChinaAsi aIntelligent SystemsMachine LearningHeNan Polytechnic University
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