{"id":16296,"date":"2017-09-25T10:30:11","date_gmt":"2017-09-25T10:30:11","guid":{"rendered":"http:\/\/biomedpharmajournal.org\/?p=16296"},"modified":"2020-04-24T15:07:51","modified_gmt":"2020-04-24T15:07:51","slug":"deblurring-of-mri-image-using-blind-and-non-blind-deconvolution-methods","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol10no3\/deblurring-of-mri-image-using-blind-and-non-blind-deconvolution-methods\/","title":{"rendered":"Deblurring of MRI Image Using Blind and Non-Blind Deconvolution Methods"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

To acquire good quality and clear image is always a challenging task. Therefore development of new and improved techniques for degradation always attract the researchers.1<\/sup>\u00a0Diagnosis through digital imaging scheme plays a dominant role in medical research, clinical practices, etc. Usually medical images such as MRI, CT scan, and X-ray are contaminated while measuring due to unknown disturbance (arising due to noise or blur) caused due to motion artifacts.2,3<\/sup> Image deblurring has many application across a large number of areas, ranging from medical imaging, microscopy,4<\/sup>\u00a0remote sensing5\u00a0<\/sup>and planetary imaging. For removing blur and noise from medical images various algorithms are proposed. Many method for blind deconvolution have been proposed but with one or other drawbacks.6\u00a0<\/sup>Basic shortcoming of earlier proposed techniques are they lack to procure the original details of the acquired images.7<\/sup><\/p>\n

Recursive soft decision approach for blind deconvolution was proposed by Kim hui yap and Ling Gaun,8\u00a0<\/sup>in which deconvolution is achieved by soft decision blur identification and hierarchical neural network. Through which limitation of hard decision method are overcome by providing a continual soft decision blur adaption according to best fit parametric structure. Molina and Katsaggelos9<\/sup> have proposed a Varitional Bayesian image restoration method10,11 <\/sup>and.12<\/sup>\u00a0This method uses product of spatially13<\/sup> weighted total variation image priors having capability of capturing local image features.14<\/sup>\u00a0Jian-Feng Cai, Hui Ji, Chaoqiang Liu, and Zuowei Shen proposed a new optimization approach,15\u00a0<\/sup>mixed regularization strategy for the blur kernel to eliminate complex motion blurring from a image by bring together new sparsity-based regularization terms on both images and motion-blur kernels.13\u00a0<\/sup>Reconstructing focused images using filtering approach was proposed by Akira Kubota and kiyoharu aizawa16<\/sup> in which degree of blur is directly and arbitrarily manipulated.<\/p>\n

In blind deconvolution method sharp version of the image is restored, without knowing the source of blurring and details of the clear image. Whereas in non-blind deconvolution blurring source and clear image is known while restoring sharp version of image. Blind deconvolution approach is more suited for practical scenario.17<\/sup>\u00a0As in real imaging world while acquiring image our image is corrupted by unknown parameter which can be Gaussian noise, atmospheric turbulence, motion blur, etc. The image capturing process is usually modeled as the convolution of a blur kernel h<\/em> with an ideal sharp image f<\/em>, plus some noise n:<\/p>\n

g = h \u2297 f + n: (1)<\/p>\n

g<\/em> is the realization of a random array with probability distribution resolute by the ideal image f <\/em>and kernel h<\/em>.<\/p>\n

In this work, firstly degradation model is described and the shortcomings of deconvolution is addressed. MRI images obtain are usually noisy or blurred. Therefore mechanism for denoising or deblurring is required. For deblurring PSF is necessary factor to be considered.18<\/sup>\u00a0Both the deconvolution models i.e. Blind deconvolution and Non Blind deconvolution performance is analyzed and compare with the help of performance parameters such as SNR, MSE and PSNR. Then after methodology adopted with simulation results and conclusion is discussed.<\/p>\n

Image Deconvolution Methods<\/strong><\/p>\n

Blind Image Deconvolution<\/strong><\/p>\n

In\u00a0blind deconvolution, deblurring of image is achieved without known point spread function. When image is acquire from the camera, it is actually a depiction of what you actually see from your naked eye. Trying to reconstruct the original image and the point spread function11<\/sup> from a camera acquired image is called blind image deconvolution.2,3<\/sup><\/p>\n

Mathematically, we wish to decompose a blurred image y as:<\/p>\n

y = k \u2297 x<\/p>\n

Where x is a visually possible sharp image and k is a non-negative blur kernel<\/p>\n

Non-Blind Deconvolution<\/strong><\/p>\n

Image deconvolution tries to obtain a sharp image f<\/em> having as input a blurred version g<\/em>, and possibly a convolution kernel h<\/em>. If h<\/em> is available, the process is called non-blind deconvolution.2,3<\/sup><\/p>\n

Mathematically represented as:<\/p>\n

g = h\u2297 f + n;<\/p>\n

Methodology Adopted<\/strong><\/p>\n

Methodology adopted for the deblurring uses blind and non-blind deconvolution algorithm. For which first, MRIscan of brain was acquired, then it was converted into grayscale and resizing of image is performed. As the size of image and type of blur in the image is the major constraint while deblurring the image. MRI image is resize to 255 x 255 pixel size. To obtain blur free image when PSF is unknown, blind deconvolution technique is utilize to produce noise free, blur free image. Whereas when PSF is known several non-blind deconvolution techniques are proposed. Usually commonly available method for deblurring utilizes non blind deconvolution technique for deblurring as it is less complex. But as we know that in practical situations type of noise and blur is not known. For finding PSF appropriate for original image in our work weighted array method is incorporated. PSF describes the degree to which system blurs a point of light. PSF can be undersized or oversized depending on the type of blur and requirement.<\/p>\n

For deconvolution known Gaussian blur was introduced on the original resize image. After which blind and non- blind deconvolution method is applied on the original and blurred image. For performance evaluation and comparison of both the methods, performanceparameter such as Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) and Mean Square Error (MSE) are computed.<\/p>\n\n\n\n
\u00a0\"Figure<\/td>\nFigure 1: Layout of methodology<\/strong><\/p>\n

 <\/p>\n

Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Simulation Result and Discussion<\/strong><\/p>\n

Image deblurring is basically the deconvolution of degraded image with the point spread function (PSF) that exactly describe the distortion. In degradation model firstly original input image is blurred or degraded through convolution with a Low pass filter, Gaussian filter is used which blur the original input image. Then after blind and non-blind deconvolution methods are applied and performance parameters SNR, PSNR and MSE are calculated. By analyzing the results we infer that blind deconvolution produces higher SNR and PSNR indicating more signal information, and lower MSE indicating less amount of error.<\/p>\n\n\n\n
\u00a0\"Figure<\/td>\nFigure 1a: Original image, (b) Blurred image, (c) Restored image, (d) Newly restored image, (e) Non blind deconvolved image<\/strong><\/p>\n

 <\/p>\n

Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Table 1: Table showing SNR, PSNR, MSE of different images<\/strong><\/p>\n\n\n\n\n\n\n\n
\u00a0<\/strong><\/td>\nSNR<\/strong><\/td>\nPSNR<\/strong><\/td>\nMSE<\/strong><\/td>\n<\/tr>\n
Original Image<\/td>\n18.434<\/td>\n33.416<\/td>\n30.535<\/td>\n<\/tr>\n
Blurred image<\/td>\n13.93<\/td>\n33.66<\/td>\n51.25<\/td>\n<\/tr>\n
Image after blind deconvolution<\/td>\n20.19<\/td>\n33.56<\/td>\n22.38<\/td>\n<\/tr>\n
Image after non blind deconvolution<\/td>\n14.90<\/td>\n33.39<\/td>\n45.82<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\u00a0\"Figure<\/td>\nFigure 2: Graphical representation of results<\/strong><\/p>\n

 <\/p>\n

Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Conclusion<\/strong><\/p>\n

In this paper a comprehensive understanding of image Deblurring technique based on Blind and non-blind Deconvolution Method are detailed. The Proposed techniques were compared for deblurring the blurred MRI image to obtain original undistorted image. Both blind and non-blind deconvolution aims to reconstruct the blurred image, blurring phenomenon can occur due many conditions such as Gaussian blur, motion artifacts, camera misfocus, etc. The result obtained by proposed techniques infers that blind deconvolution approach is more suitable and appropriate both practically and experimental. From the simulation resultwe found that the blind deconvolution approach provides the better results in restoring the original MRI image from blur image. For blind deconvolution method we obtained higher PSNR and SNR value compared to that of non- blind deconvolution method, which indicates improved quality of image as depicted in table 1. MSE value for blind deconvolution is also lesser than other method, which signifies small error is present in the reconstructed image.<\/p>\n

References<\/strong><\/p>\n

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