Since noise degrades the accuracy and precision of DNA capillary electrophoresis (CE) analysis, signal denoising is thus important to facilitate the postprocessing of CE data. In this paper, a new denoising algorithm based on dyadic wavelet transform using multiscale products is applied for the removal of the noise in the DNA CE signal. The adjacent scale wavelet coefficients are first multiplied to amplify the significant features of the CE signal while diluting noise. Then, noise is suppressed by applying a multiscale threshold to the multiscale products instead of directly to the wavelet coefficients. Finally, the noise-free CE signal is recovered from the thresholded coefficients by using inverse dyadic wavelet transform. We compare the performance of the proposed algorithm with other denoising methods applied to the synthetic CE and real CE signals. Experimental results show that the new scheme achieves better removal of noise while preserving the shape of peaks corresponding to the analytes in the sample.
An efficient SAR image fusion algorithm for multipolarimetric images based on Directionlets transform is proposed. Directionlets transform is a new lattice-based multiscale analysis anisotropic multi-directional wavelet transform. Firstly, several polarimetric images can be decomposed into lowfrequency coefficients and high-frequency coefficients with multiscales and multi-directions using the Directionlets transform. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the directive contrast and the larger value of region variance information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused Directionlets coefficients.Experimental results show that compared with traditional algorithm, the proposed algorithm can get better visual effect and the significant information of original image like textures and contour details is well maintained.
2D animation production has always been time-consuming and labor-intensive. This thesis investigates several processes in the animation production pipeline that are especially laborious. A 2D animation system has been created to provide more efficient method of performing those tasks. A tremendous amount of research work has been done in the creation of the system, including user-controllable automatic inbetween generation, automatic coloring, smart stroke input, high quality stroke rendering to provide accurate visual feedback and distributed framework to reduce computational time. Inbetweening is one of the most time-consuming processes of 2D animation production. Automatic inbetween generation addresses this issue, but it remains a tough issue in research. Most existing methods apply interpolation based on either pixel intensity of raster images or mathematical functions of vector representation, but neglect information in the drawing itself, e.g. shapes or motion of characters. That often results in distorted lines and shapes as well as unsmooth motion. This thesis contributes significant improvements to automatic inbetween generation algorithms. They include parameterized non-linear interpolation, feature point preservation and junction preservation. Coloring is another time-consuming process in 2D animation production. Great amount of research has been done intending to achieve automatic frame generation. This thesis proposes vector-based region recognition and region matching algorithms.
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