A Neural Approach to Blind Motion Deblurring
We present a new method for blind motion deblurring that uses a neural network trained to compute estimates of sharp image patches from observations that are blurred by an unknown motion kernel. Instead of regressing directly to patch intensities, this network learns to predict the complex Fourier coefficients of a deconvolution filter to be applied to the input patch for restoration. For inference, we apply the network independently to all overlapping patches in the observed image, and average its outputs to form an initial estimate of the sharp image. We then explicitly estimate a single global blur kernel by relating this estimate to the observed image, and finally perform non-blind deconvolution with this kernel. Our method exhibits accuracy and robustness close to state-of-the-art iterative methods, while being much faster when parallelized on GPU hardware.
Code (2)
Tasks
DeblurringGPUSimilar Papers 제목 키워드 기반
Deep learning in motion deblurring: current status, benchmarks and future prospects
Motion deblurring is one of the fundamental problems of computer vision and has received continuous attention. The variability in blur, both within and across images, imposes limitations on non-blind deblurring technique…
DeblurringDeep LearningBlur-Invariant Deep Learning for Blind-Deblurring
In this paper, we investigate deep neural networks for blind motion deblurring. Instead of regressing for the motion blur kernel and performing non-blind deblurring out- side of the network (as most methods do), we propo…
DeblurringDecoderDeep LearningSL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse Learning
In this paper, we introduce an end-to-end generative adversarial network (GAN) based on sparse learning for single image blind motion deblurring, which we called SL-CycleGAN. For the first time in blind motion deblurring…
DeblurringGenerative Adversarial NetworkImage DeblurringSparse Learning+1Divide and Conquer for Full-Resolution Light Field Deblurring
The increasing popularity of computational light field (LF) cameras has necessitated the need for tackling motion blur which is a ubiquitous phenomenon in hand-held photography. The state-of-the-art method for blind debl…
CPUDeblurringLight Field Blind Motion Deblurring
We study the problem of deblurring light fields of general 3D scenes captured under 3D camera motion and present both theoretical and practical contributions. By analyzing the motion-blurred light field in the primal and…
Deblurring