PS estimation for image deblurring
CVGIP: Graphical Models and Image Processing
Motion-Based Motion Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer
IEEE Transactions on Computers
Regularized constrained total least squares image restoration
IEEE Transactions on Image Processing
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The paper addresses a commonly encountered motion blur problem in photographic images, when there is relative motion between the camera and the object being captured. When a photograph is taken in low light conditions or of a fast moving object, motion blur can cause significant degradation of the image. Both the moving object and camera shake contribute to this blurring. The overall approach comprises of taking a standard (non-blurred) image, creating a known blurring function (point spread function-PSF) and then filtering the image with this function so as to add blur into it. This image is further corrupted by different amount of additive Gaussian noise. The aim is to deblur this image by various deblurring algorithms viz., direct and pseudo-inverse filtering, Wiener and parametric Wiener filtering, constrained least squares filtering and Richardson-Lucy algorithm, then analyze and compare their properties. Experimental evaluation is carried out in MATLAB environment on standard lena and cameraman images and these methods are compared in a variety of blur and noise conditions. Both qualitative and quantitative assessment based on popular performance metrics in image processing i. e., peak signal-to-noise ratio (PSNR) and mean squared error (MSE), provides an objective and subjective standards to compare the deblurring methods.