The conjugated null space method of blind PSF estimation and deconvolution optimization
We have shown that the vector of the point spread function (PSF) lexicographical presentation belongs to the left side conjugated null space (NS) of the autoregression (AR) matrix operator on condition the AR parameters are common for original and blurred images. The method of the PSF and inverse PSF (IPSF) evaluation in the basis of the NS eigenfunctions is offered. The optimization of the PSF and IPSF shape with the aim of fluctuation elimination is considered in NS spectral domain and image space domain. The function of surface area was used as the regularization functional. Two methods of original image estimate optimization were designed basing on maximum entropy generalization of sought and blurred images conditional probability density and regularization. The first method uses balanced variations of convolutions with the PSF and IPSF to obtaining iterative schema of image optimization. The variations balance is providing by dynamic regularization basing on condition of the iteration process convergence. The regularization has dynamic character because depends on current and previous image estimate variations. The second method implements the regularization of the deconvolution optimization in curved space with metric defined on image estimate surface. The given iterative schemas have fast convergence and therefore can be used for reconstruction of high resolution images series in real time. The NS can be used for design of denoising bilateral linear filter which does not introduce image smoothing.
Code (0)
등록된 구현이 없습니다.
Tasks
Denoisingimage smoothingSimilar Papers 제목 키워드 기반
Photon-Limited Blind Deconvolution using Unsupervised Iterative Kernel Estimation
Blind deconvolution is a challenging problem, but in low-light it is even more difficult. Existing algorithms, both classical and deep-learning based, are not designed for this condition. When the photon shot noise is st…
Image RestorationSemi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks
We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regular…
regressionKernel 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. Conventionally, these problems are solved by alternating between estimation of the…
Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer
Deconvolution microscopy has been extensively used to improve the resolution of the widefield fluorescent microscopy. Conventional approaches, which usually require the point spread function (PSF) measurement or blind es…
Cycle-Space Informed Detection of Autoencoded Blind False Data Injection Attacks on Power Systems
The rapid growth of AI-driven data centers and large-scale energy storage systems is increasing the reliance of power system operation on real-time measurement data and automated decision-making. However, many existing d…