DWT-FFC and NTIRE-2021-Dehazing-DWGAN
These two tools are competitors, representing different state-of-the-art dehazing solutions that won the same challenge in different years (2021 and 2023), each offering a distinct architecture (DWT-FFC versus DW-GAN) for the task.
About DWT-FFC
zhouh115/DWT-FFC
Official PyTorch implementation of dehazing method based on FFC and ConvNeXt, 1st place solution of NTIRE 2023 HR NonHomogeneous Dehazing Challenge (CVPR Workshop 2023).
This tool helps improve the clarity of images taken in hazy or foggy conditions, especially when the haze isn't uniform across the scene. You input a hazy image, and it outputs a clearer, dehazed version. This is useful for anyone working with outdoor photography, surveillance, remote sensing, or autonomous vehicle perception where atmospheric conditions obscure important visual details.
About NTIRE-2021-Dehazing-DWGAN
liuh127/NTIRE-2021-Dehazing-DWGAN
Official PyTorch implementation of DW-GAN, 1st place solution of NTIRE 2021 NonHomogeneous Dehazing Challenge (CVPR Workshop 2021).
This tool helps improve the clarity of images taken in hazy conditions, especially when the haze is unevenly distributed across the scene. It takes hazy images as input and produces clearer, dehazed images, making details more visible. This is useful for anyone working with outdoor photography, surveillance, or remote sensing where atmospheric haze obscures visual information.
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