Search results
Results from the WOW.Com Content Network
NTIRE 2020 NonHomogeneous Dehazing Challenge (CVPR Workshop 2020) Winner Award Solution. - GlassyWu/KTDN
In this paper, we propose a knowledge transfer method that utilizes abundant clear images to train a teacher network to provide strong and robust image prior. The derived architecture is referred to as the Knowledge Transform Dehaze Network (KTDN), which consists of the teacher network and the dehazing network with identical architecture.
KTDN: Knowledge Transfer Dehazing Network for NonHomogeneous Dehazing. SRKTDN: Applying Super Resolution Method to Dehazing Task. [code] DALF: A guiding teaching and dual adversarial learning framework for a single image dehazing.
results, the KTDN ranks 2nd in NTIRE-2020 NonHomoge-neous Dehazing Challenge [4, 5]. Insummary,thecontributionsofourworkareasfollows: 1. We propose a knowledge transfer method for image dehazing with a dual network. The teacher network learned the distributions of clear images via image re-construction task, and has ability to provide favorable
Login. Welcome to the Kal Tire Distribution Network
In this paper, we propose a knowledge transfer method that utilizes abundant clear images to train a teacher network to provide strong and robust image prior. The derived architecture is referred to as the Knowledge Transform Dehaze Network (KTDN), which consists of the teacher network and the dehazing network with identical architecture.
In this paper, we propose a knowledge transfer method that utilizes abundant clear images to train a teacher network to provide strong and robust image prior. The derived architecture is referred to as the Knowledge Transform Dehaze Network (KTDN), which consists of the teacher network and the dehazing network with identical architecture.