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By concerning with the health of the patients, analysis of blood cell particularly morphological structure of leukocyte in microscopic blood smear can effectively detect the important blood disorder such as the Acute Lymphoblastic Leukemia. Unfortunately, the analysis made by hematology expert is not always accurate and rapid due to the error prone modality and operator's incapability's. The presented...
In medical applications, detection and outlining of boundaries of organs and tumors are standard requirements, and hence the problem of detection of edges in images is one of important problems in computerized tomography (CT). The process reconstruction of images from projections first and then the detection of edges from the images so reconstructed is computationally expensive. Thus the detection...
Image deblurring and denoising are the main steps in early vision problems. A common problem in deblurring is the ringing artifacts created by trying to restore the unknown point spread function (PSF). The random noise present makes this task even harder. Variational blind deconvolution methods add a smoothness term for the PSF as well as for the unknown image. These methods can amplify the outliers...
Using the edge detection techniques we propose a new enhancement scheme for noisy digital images. This uses inhomogeneous anisotropic diffusion scheme via the edge indicator provided by well known edge detection methods. Addition of a fidelity term facilitates the proposed scheme to remove the noise while preserving edges. This method is general in the sense that it can be incorporated into any of...
This paper presents a technique for performing unsupervised clustering of satellite images using a unique 'sampling-resampling' based Bayesian learning method. The multi-band pixel values of the satellite image are expected to form a certain number of clusters. The parameters of these clusters are learnt using a Bayesian approach. This technique is unsupervised in the sense that no separate training...
A new class of variational-PDE based models is proposed for edge preserving image enhancement. Using the equivalence relation between regularization and PDEs we propose a general class of models which restore noisy images with selective enhancement. It contains a controlled inverse diffusion term which sharpens the image. Experiments show the effectiveness of the model with real and MRI images.
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