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[TPAMI 2023 / ACMMM 2022 Best Paper Runner-Up Award] Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling (a Data Perspective)
Learning image-to-image translation using paired and unpaired training samples
This project implements a CycleGAN-based method for image dehazing on a paired dataset containing hazy images and their corresponding ground truth clear images.