Point Spread Function

This paper attempts to undertake the study of Restored Gaussian Blurred Images. by using four types of techniques of deblurring image as Wiener filter, Regularized filter, Lucy Richardson deconvlutin algorithm and Blind deconvlution algorithm with an information of the Point Spread Function (PSF) corrupted blurred image with Different values of Size and Alfa and then corrupted by Gaussian noise. The same is applied to the remote sensing image and they are compared with one another, So as to choose the base technique for restored or deblurring image.This paper also attempts to undertake the study of restored Gaussian blurred image with no any information about the Point Spread Function (PSF) by using same four techniques after execute the guess of the PSF, the number of iterations and the weight threshold of it. To choose the base guesses for restored or deblurring image of this techniques.

In a conventional single-exposure photograph, moving objects or moving cameras cause motion blur. The exposure time defines a temporal box filter that smears the moving object across the image by convolution. This box filter destroys important high-frequency spatial details so that deblurring via deconvolution becomes an illposed problem. Rather than leaving the shutter open for the entire exposure duration, we ¨flutter¨the camera´s shutter open and closed during the chosen exposure time with a binary pseudo-random sequence. The flutter changes the box filter to a broad-band filter that preserves high-frequency spatial details in the blurred image and the corresponding deconvolution becomes a well-posed problem. We demonstrate that manually-specified point spread functions are sufficient for several challenging cases of motion-blur removal including extremely large motions, textured backgrounds and partial occluders.

Removing non-uniform blur and noise from optical images is a very dicult,problem to resolve. In this paper we describe a strategy that can be used for solving such problems. We describe how to restore images blurred by an unknown,spatially-varying point spread function (PSF) by using a combination of methods including sectioning and phase diversity blind deconvolution. The PSFs on

The Image restoration is the process of recovery of an image that has been corrupted by some degradation phenomenon. Degradation occurs due to motion blur, gaussian blur, noise and camera mismatch. In this paper an attempt has been made to recover image from the corrupted image using Particle Swarm Optimization (PSO) algorithm in the presence of gaussian blur. For this purpose, a heuristic particle swarm optimization technique has been developed to optimize the parameters of the Point Spread Function (PSF). Higher resolution, better quality image is obtained by deblurring the noisy/ blurred image using this method. The algorithm performance is compared with Lucy Richardson algorithm. Experimental results indicated that the PSO regularized technique will improve the image quality significantly. Better results in terms of PSNR, SNR and image quality index are achieved.

The application of the hyperacuity technique to image processing of star trackers is analysed. An analytical study of the error introduced by the centroiding algorithm is presented and it is shown that a systematic contribution and a random one exist. They result from image processing assumptions and photometric measure uncertainty, respectively. Their behaviour is characterised by means of numerical simulations that are based on optics theoretical point spread functions. The latter ones take into account both defocus and diffraction effects. First, measured star position uncertainty is evaluated as a function of defocus. As a result, a criterion for optimal defocus is presented. Subsequently, an original procedure for systematic centroiding error correction by means of a backpropagation neural network is described. It is also suitable for real hardware calibration. When applied to one of the considered numerical models, the position computation accuracy is improved from 0.01 to 0.005 pixels.

Fluorescent imaging microscopy has been an essential tool for biologists over many years, especially after the discovery of the green fluorescent protein and the possibility of tagging virtually every protein with it. In recent years dramatic enhancement of the level of detail at which a fluorescing structure of interest can be imaged have been achieved. We review classical and new developments in high-resolution microscopy, and describe how these methods have been used in biological research. Classical methods include widefield and confocal microscopy whereas novel approaches range from linear methods such as 4Pi, I(5) and structured illumination microscopy to non-linear schemes such as stimulated emission depletion and saturated structured illumination. Localization based approaches (e.g. PALM and STORM), near-field methods and total internal refraction microscopy are also discussed. As the terms 'resolution', 'sensitivity', 'sampling' and 'precision&#3...

In this paper we investigate the problem of recovering the motion blur point spread function (PSF) by fusing the information available in two differently exposed im- age frames of the same scene. The proposed method exploits the difference between the degradations which affect the two images due to their different exposure times. One of the images is mainly affected by noise due to low exposure whereas the other one is mainly af- fected by motion blur caused by camera motion during the exposure time. Assuming certain models for the ob- served images and the blur PSF, we propose a maximum a posteriory (MAP) estimator of the motion blur. The experimental results show that the proposed method has the ability to estimate the motion blur PSF caused by rather complex motion trajectories, allowing a signifi- cant increase in the signal to noise ratio of the restored image.

We describe the change of the spatial distribution of the state of polarisation occurring during two-dimensional imaging through a multilayer and in particular through a layered metallic flat lens. Linear or circular polarisation of incident light is not preserved due to the difference in the amplitude transfer functions for the TM and TE polarisations. In effect, the transfer function and the point spread function that characterize 2D imaging through a multilayer both have a matrix form and cross-polarisation coupling is observed for spatially modulated beams with a linear or circular incident polarisation. The point spread function in a matrix form is used to characterise the resolution of the superlens for different polarisation states. We demonstrate how the 2D PSF may be used to design a simple diffractive nanoelement consisting of two radial slits. The structure assures the separation of non-diffracting radial beams originating from two slits in the mask and exhibits an interesting property of a backward power flow in between the two rings.

Acquiring photographs as input for an image-based modelling pipeline is less trivial than often assumed. Photographs should be correctly exposed, cover the subject sufficiently from all possible angles, have the required spatial resolution, be devoid of any motion blur, exhibit accurate focus and feature an adequate depth of field. The last four characteristics all determine the " sharpness " of an image and the photogrammetric, computer vision and hybrid photogrammetric computer vision communities all assume that the object to be modelled is depicted " acceptably " sharp throughout the whole image collection. Although none of these three fields has ever properly quantified " acceptably sharp " , it is more or less standard practice to mask those image portions that appear to be unsharp due to the limited depth of field around the plane of focus (whether this means blurry object parts or completely out-of-focus backgrounds). This paper will assess how well-or ill-suited defocus estimating algorithms are for automatically masking a series of photographs, since this could speed up modelling pipelines with many hundreds or thousands of photographs. To that end, the paper uses five different real-world datasets and compares the output of three state-of-the-art edge-based defocus estimators. Afterwards, critical comments and plans for the future finalise this paper.