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Models, dataset, results
Repository for Realistic Blur Synthesis for Learning Image Deblurring
We construct a large-scale multi-cause (MC-Blur) dataset for image deblurring. It consists of four blur types: uniform blurs, motion blurs by averaging continuous frames, heavy defocus blurs, and real-world blurs. We collect these images from more than 1000 diverse scenes such as buildings, city…
The main features of hdcvlab/mc-blur-dataset are: Motion Deblurring.
Projects with overlapping indexed features include: greatlog/realdan — Official implementation of our IJCV paper. hdcvlab/deblurring-by-realistic-blurring — Models, dataset, results. leiali/reloblur — Paper | Project Page | Video. rimchang/rsblur — Repository for Realistic Blur Synthesis for Learning Image Deblurring. zzh-tech/bit — [CVPR2023] Blur Interpolation Transformer for Real-World Motion from Blur. zzh-tech/estrnn — [IJCV][ECCV2020 Spotlight] Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring.
