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• W2892059392 abstract "Single haze removal is a challenging ill-posed problem. Most existing methods solving this dilemma depend on atmospheric physical scattering model. In other words, they recover haze-free images by estimating the atmospheric transmission. In this paper, we proposed a new recovery model called Residual Adding model, which takes dehazing procedure as a hazy image adding a loss image. Based on this new model, we proposed a single image dehazing network built with Conditional Generative Adversarial Nets (CGAN), called Residual Learning Dehazing Network (RLD-Net). Benefiting from the new model, the RLD-Net is designed as not only an end-to-end dehazing network but also a point-to-point mapping network. That means RLD-Net can take a hazy image as input and a corresponding clear image as output without any extract calculation like inversing atmospheric physical scattering model. Experimental results on both synthesized hazy images and real-world hazy images demonstrate our outstanding performance." @default.
• W2892059392 created "2018-09-27" @default.
• W2892059392 creator A5063717271 @default.
• W2892059392 creator A5077029839 @default.
• W2892059392 date "2018-01-01" @default.
• W2892059392 modified "2023-10-06" @default.
• W2892059392 title "Residual Learning Dehazing Net" @default.
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• W2892059392 doi "https://doi.org/10.1007/978-3-030-00776-8_13" @default.
• W2892059392 hasPublicationYear "2018" @default.
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