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In this paper, we propose a new image denoising method based on wavelet thresholding. In this method, we introduce a new nonlinear thresholding function characterized by a shape parameter and basic properties. These characteristics make the new method able to achieve a compromise between both traditional thresholding techniques such as Hard and Soft thresholding. The experimental results show that our proposed method provides better performance compared to many classical thresholding methods in terms of the visual quality of the denoised image.
In contrast limited adaptive histogram equalization (CLAHE), the selection of tile size, clip-limit and the distribution which specify desired shape of the histogram of image tiles is paramount, as it critically influences the quality of the enhanced image. The optimal value of these parameters devolves on the generic of the image to be enhanced and usually they are selected empirically. In this paper, the degradation of intensity, textural and geometric features of the medical image with respect to the variation in clip-limit and specified histogram shape is analyzed. The statistical indices used to quantify the feature degradation are Absolute Mean Brightness Error (AMBE), Absolute Deviation in Entropy (ADE), Peak Signal to Noise Ratio (PSNR), Variance Ratio (VR), Structural Similarity Index Matrix (SSIM) and Saturation Evaluation Index (SEI). The images used for the analysis are axial plane MR images of magnetic resonance spectroscopy (MRS), under gradient recalled echo (GRE), diffusion weighted imaging (DWI) 1000b Array Spatial Sensitivity Encoding Technique (ASSET), T2 Fluid Attenuation Inversion Recovery (FLAIR) and T1 Fast Spin-Echo Contrast Enhanced (FS-ECE) series of pre-operative Glioblastoma-edema complex. The experimental analysis was performed using Matlab®. Results show that for MR images the exponential histogram specification with a clip-limit of 0.01 is found to be optimum. At optimum clip-limit, the mean of SSIM exhibited by the Rayleigh, uniform and exponential histogram specification were found to be 0.7477, 0.7946 and 0.8457, for ten sets of MR images and mean of variance ratio are 1.242, 2.0316 and 1.7711, respectively.
This paper reports an adaptive three dimensional discrete cosine transform (3D-DCT) based motion level prediction algorithm which determines the optimal cube for 3D-DCT based compression technique by analyzing the motion content of the video sequence. Irrespective of the motion levels in the video sequence the generally used cube size is [8 × 8 × 8], but the proposed algorithm reported in this paper will adaptively choose the cube size in relation to the motion level of video sequence. The effectiveness of the algorithm can be verified by performing Rate Vs distortion comparison with different motion level sequences. Peak Signal to Noise Ratio (PSNR) has been taken as a measure of distortion. Experimental results reveals that without any motion compensation technique ,the proposed 3D-DCT algorithm which adaptively selects the cube size relative to the motion content of video sequence gives better performance in terms of reduction in the data rate and speed up the encoding process compared to the existing 3D-DCT based video compression algorithm.
W artykule opisano algorytm kompresji video bazujący na adaptacyjnej trójwymiarowej dyskretnej transformacie kosinusowej i przewidywaniu poziomu ruchu. W sposób adaptacyjny dobierany jest rozmiar sześcianu. Weryfikacji dokonano przez porównanie szybkości względem zniekształceń dla różnych sekwencji ruchu.
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