Research On Restoration Methods Of SAR Images And Medical Ultrasound Images

In various types of image systems,images are always interfered by internal and external disturbances in the process of acquisition and transmission,causing images to be blurred or have noise,which seriously affects the image quality.Therefore,image restoration is crucial in image processing.Synthetic Aperture Radar(SAR)is a kind of ground-based observation system using electromagnetic waves.The obtained SAR image often contains strong speckle noise,which makes it difficult to identify targets in the image.Medical ultrasound imaging is a diagnostic method for the diagnosis of diseases using ultrasound.The acquired ultrasound images often contain speckle noise,which seriously affects the clinical diagnosis.Therefore,this dissertation mainly studies the denoising and deblurring methods for SAR images and medical ultrasound images.The work is summarized as follows:Firstly,for SAR images,this dissertation studies a new convex variational model for denoising and deblurring images with multiplicative noise.Based on the statistical property of the multiplicative noise from Nakagami distribution,the denoising model minimizes the sum of a data fidelity term,a quadratic penalty term and a total-variation regularization term.Here,the quadratic penalty term is mainly adopted to guarantee the model to be strictly convex under a mild condition.Furthermore,the model is extended for the simultaneous denoising and deblurring case by introducing a blurring operator.The dissertation also studies some mathematical properties of the proposed model.In addition,the model is solved by applying the primal-dual algorithm.The experimental results show that the proposed method is promising in restoring(blurred)images with multiplicative noise.Secondly,for medical ultrasound images,this dissertation proposes a noval non-smooth non-convex variational model for ultrasound images denoising and deblurring motivated by the successes of sparse representation of images and FoE based approaches.Dictionaries are well adapted to textures and extended to arbitrary image sizes by defining a global image prior,while FoE image prior explicitly characterizes the statistics properties of natural images.Following these ideas,the new model is composed of the data-fidelity term,the sparse andredundant representations via learned dictionaries,and the FoE image prior model.The K-SVD algorithm and i Piano algorithm can efficiently deal with this optimization problem.The new proposed model is applied to simulated images and real ultrasound images.The experimental results of denoising and deblurring show that proposed method gives a better visual effect by efficiently removing noise and preserving details well compared with two state-of-the-art methods.