It is very interesting to reconstruct high-resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super-resolution method for CT...
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It is very interesting to reconstruct high-resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super-resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled K-singular value decomposition (ksvd) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low-resolution images, the authors prepare dictionaries over LR and HR image patches using the ksvd algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal-to-noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high-resolution image. These parameters play an essential role in the reconstruction of the HR images.
A new baseline correction algorithm for spectral signal based on sparse representation is proposed. Firstly, utilizing the training sample to obtain the dictionaries of both baseline and spectrum;Secondly, establishin...
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A new baseline correction algorithm for spectral signal based on sparse representation is proposed. Firstly, utilizing the training sample to obtain the dictionaries of both baseline and spectrum;Secondly, establishing sparse representation model of spectral signal;thirdly, employing OM-P algorithm to calculate the representation coefficients of spectral signal and finally, obtaining the spectral baseline from representation coefficients which are corresponded to the baseline dictionary. Then, the spectra baseline correction is completed by removing the baseline from original observed spectrum. Contrast experiment and quantitative analysis of corrected spectral signals are conducted and results show the highly efficiency and accuracy of the proposed algorithm.
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