Multiwavelets Construction And Application In Image Processing

To solve the question on correctly choosing the multiwavelets according signal and image contents and their processing intention, the construction methods and application of multiwavelets and prefilter are studied. The dissertation mainly included following aspects:1. Five characteristics describing multiwavelets are summarized, which are orthogonality, symmetry, approximation, regularity and time-frequency resolution. A connection between the multiwaveles design and application are established by these characteristics.2. The multiwavelets construction methods are introduced according the desire performance of the multiwavelets characteristics, which are orthogonal, symmetric, high approximation, good regularity and optimum time-frequency resolution. The feasibility of these methods is analyzed. The concrete means are given to construct wavelets. Correctly initializtion parameters and varying steps overcome the complexity of computation and local minimum. A convenient multiwavelets construction tool is provided for the practical design process.3. The correct selection of prefilter is critical to the performance of the multiwavelets. Three design schemes of prfilters are introduced, which are vector prefilter, prefilter groop, balanced multiwavelets . The concrete realization methods are proposed. A null space method on computing Q (0) is present to boost the computation velocity and correctness.4. A criterion of choosing multiwavelets in multi-focus image fuse is proposed, which is the wavelet functions should have narrow frequency width and high time frequency resolution. A new fusion rule is proposed for multi-focus image fusion. The object evaluation of fuse result is analyzed. The square root error between the original clarity image and the fusing result image is an excellent criterion for evaluating the fuse result. Without the original clarity image, the spatial frequency and definition can be used only when the fusing result image is good subjectively.5. The selecting method of multiwaveletsin image denoising is present. At the same threshold function and threshold value conditions, the higher time frequency resolution, the better the denoising effect is.6. The phytoplankton cell image edge and shape detecting method based on mu1tiwavelets is studied by analyzing the characteristics of the phytoplankton cell image edge. The experiments prove the validity of the methods.