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Found 3 papers, 3 papers with code
Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks
Then, a second network trained jointly with the first one, unrolls a non-blind deconvolution method using the motion kernel field estimated by the first network.
Rethinking Motion Deblurring Training: A Segmentation-Based Method for Simulating Non-Uniform Motion Blurred Images
Successful training of end-to-end deep networks for real motion deblurring requires datasets of sharp/blurred image pairs that are realistic and diverse enough to achieve generalization to real blurred images.
Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition
In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images.
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