DeblurFlow reformulates flow matching trajectories so the vector field matches the blur-to-clean residual, enabling LoRA-adapted pretrained flow models to perform blind motion deblurring with both high PSNR and perceptual quality.
Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.IEEE Transactions on Image Processing, 26(7):3142–3155
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Weighted Reverse Convolution is a spatially adaptive inverse operator for densifying high-level visual descriptors from vision foundation models, using weighted regularization and an FFT closed-form solution to improve dense prediction tasks.
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Restoration-Aligned Generative Flow Models for Blind Motion Deblurring
DeblurFlow reformulates flow matching trajectories so the vector field matches the blur-to-clean residual, enabling LoRA-adapted pretrained flow models to perform blind motion deblurring with both high PSNR and perceptual quality.
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Weighted Reverse Convolution for Feature Upsampling
Weighted Reverse Convolution is a spatially adaptive inverse operator for densifying high-level visual descriptors from vision foundation models, using weighted regularization and an FFT closed-form solution to improve dense prediction tasks.