In this paper we propose new blur identi cation algorithm based on singular value decomposition (SVD) of degraded image. An unknown space-invariant point-spread function (PSF) is also decomposed using SVD. Magnitude functions of PSF singular vectors (left and right) are identi ed using averaged spectra of corresponding singular vectors of degraded image. Phase functions of PSF singular vectors ...

The averaged point-spread function (PSF) estimation of an image acquisition system is important for many computer vision applications, including edge detection and depth from defocus. The paper compares several mathematical models of the PSF and presents an improved measurement technique that enables subpixel estimation of 2D functions. New methods for noise suppression and uneven illumination ...

This paper proposes a new covariance matching based technique for blurred image PSF (point spread function) estimation. A patch based image degradation model is proposed for the covariance matching estimation framework. A robust covariance metric which is based on Riemannian manifold is adapted to measure the distance between covariance matrices. The optimal PSF is computed by minimizing the di...

This paper addresses the problem of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algori...

We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variationa...

The estimation of the point spread function (PSF) for blur identification, often a necessary first step in the restoration of real images, method is presented. The PSF estimate is chosen from a collection of candidate PSFs, which may be constructed using a parametric model or from experimental measurements. The PSF estimate is selected to provide the best match between the restoration residual ...

Cosmic shear requires high precision measurement of galaxy shapes in the presence of the observational point spread function (PSF) that smears out the image. The PSF must therefore be known for each galaxy to a high accuracy. However, for several reasons, the PSF is usually wavelength dependent; therefore, the differences between the spectral energy distribution of the observed objects introduc...

An image captured by a sensor is not an exact reproduction of the scene viewed. If a point of light is viewed, the optical system will blur or spread the light to some degree, characterised by the point spread function (PSF). In the mathematical model of the imaging system, the PSF is a weighting function for spatial convolution [94, pp. 7891]. It can be considered as the spatial responsiveness...

To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and bene...

Conventional point spread function (PSF) measurement methods often use parametric models for the estimation of the PSF. This limits the shape of the PSF to a specific form provided by the model. However, there are unconventional imaging systems like multispectral cameras with optical bandpass filters, which produce an, e.g., unsymmetric PSF. To estimate such PSFs we have developed a new measure...