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Found 7 papers, 3 papers with code
Kernel Diffusion: An Alternate Approach to Blind Deconvolution
Blind deconvolution problems are severely ill-posed because neither the underlying signal nor the forward operator are not known exactly.
The Secrets of Non-Blind Poisson Deconvolution
Non-blind image deconvolution has been studied for several decades but most of the existing work focuses on blur instead of noise.
Structured Kernel Estimation for Photon-Limited Deconvolution
By modeling the blur kernel using a low-dimensional representation with the key points on the motion trajectory, we significantly reduce the search space and improve the regularity of the kernel estimation problem.
Photon-Limited Blind Deconvolution using Unsupervised Iterative Kernel Estimation
When the noise is strong, these networks fail to simultaneously deblur and denoise; (3) While iterative schemes are known to be robust in the classical frameworks, they are seldom considered in deep neural networks because it requires a differentiable non-blind solver.
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement
Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization.
Photon Limited Non-Blind Deblurring Using Algorithm Unrolling
Image deblurring in photon-limited conditions is ubiquitous in a variety of low-light applications such as photography, microscopy, and astronomy.
Photon-Limited Deblurring using Algorithm Unrolling
Image deblurring in a photon-limited condition is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy.
