Abstract
Objectives: To develop image deblurring algorithm to effectively recover the original image from the captured blurry and noisy image. Methods: A novel image deblurring algorithm using adaptive priors is proposed. The adaptive priors vary the sparsity induced based on factors such as the type of blur affecting the image, image region, statistical parameters regarding the blur kernel and/or statistical predictions on the blur kernel. The proposed algorithm using adaptive priors improves the quality of the deblurred result, when compared with various recent image deblurring algorithms. Finding: the work contains a case study with regard to certain standard parameters. It is observed that proposed method is better in terms of frequency response, Peak signal-tonoise ratio (PSNR) and Structural Similarity Index (SSIM) values in comparison with other priors. Novelty: the proposed priors lead to the most effective results for image deblurring using the Bayesian framework. The proposed method improves the performance by 30% in PSNR and 45% in SSIM values in dB with uniform kernel size 12 * 12 and improves performance by 30 % in PSNR and 32% SSIM with a standard deviation of 3.5. The proposed method enhances the frequency response of the real-time image restoration process. Applications: Some important applications include the restoration of medical images such as MRI images, CT images where the intensity of these radiations is maintained to avoid damage to human organs. Remote sensing images captured through drones at a specified time cannot be retaken and hence restoring such images is very important. Similarly restoring images from CCTV footage, astronomical images. Keywords: Blind Image Deblurring; Maximum a Posteriori Estimation; Image priors; Regularization; Structural Similarity Index (SSIM) and Peak signal-to-noise ratio (PSNR)
Highlights
In many image restoration and image deblurring algorithms, the image deblurring limitation assumes that there exists a linear shift invariant blur kernel caused due to the degradation process, known as point spread function (PSF)
The performance of the proposed novel image prior (SAP) in an image deblurring algorithm using the Maximum A Posterior (MAP) mathematical model is compared with the popular generic image priors used in the MAP model such as the Tikhonov (L2), Sobolev, Total variational (TV), Sparse, Sparse and redundant prior
The investigation on performance of the proposed method is done through the use of performance metrics such as the Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index (SSIM)
Summary
In many image restoration and image deblurring algorithms, the image deblurring limitation assumes that there exists a linear shift invariant blur kernel caused due to the degradation process, known as point spread function (PSF). If PSF is not known, the limitation is termed as blind image deconvolution. Blind image deconvolution techniques capable of handling a large motion blur have been designed. If the camera shakes when a picture is taken, a space-variant blurring occurs due to non-negligible depth variation. The recorded picture is blurred version of real image representing the actual scene in most of the imaging applications. Blurring is caused due to various reasons like optical aberrations, atmospheric distortions, averaging on pixel site on sensor, motion of objects/persons in the scene and motion of camera. The problem of restoration is ill-posed and requires techniques of regularization to effectively restore the image
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