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Research On Image Processing Algorithms Via Multiscale Geometric Analysis

Posted on:2009-10-16Degree:DoctorType:Dissertation
Country:ChinaCandidate:W NiFull Text:PDF
GTID:1118360302469119Subject:Circuits and Systems
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Based on computational harmonic analysis, a novel Multiscale Geometric Analysis (MGA) theory was proposed to capture the geometrical structure in visual information efficiently, it can achieve optimal approximation behavior for 2-D piecewise smooth function, thus obtaining highly efficient representation and processing methods. Wavelet is an optimal tool only for 1-D piecewise smooth signals, As a result of a separable extension from 1-D bases, wavelet in 2-D is far from optimal. The disappointing behaviors of wavelets have led to a series of MGA tools. MGA theory and its applications still need further research and development. In this dissertation, the author mainly focuses on the research of Contourlet transform and the key techniques of its applications in image processing. As the latest MGA tool, Contourlet is a"true"two dimensional sparse representation, it provides much better anisotropy, multiresolution, directionality and localization properties for 2-D signals than existing image representation methods. Therefore, Contourlet is more appropriate for various image processing tasks and better performance would be expected. The main research work in the dissertation is as follows:1. Detailed analysis is provided for the advantages of MGA from the view of statistics of natural image and the functionality of human vision system. Based on detailed comparison, the intrinsic reasons are given. The advantages of multiscale geometric analysis make it able to capture high dimensional singularities in natural image efficiently, and also consistent with the perception and integration principle of receptive field of visual cortex in human vision system.2. A systematical research of the development and common analysis tools are given firstly, including Ridgelet, Curvelet, Beamlet, Bandelet, etc. For Contourlet transform and its novel extension—Nonsubsampled Contourlet Transform, we provide a detailed analysis to its basic principles, implementation schemes, advantages and disadvantages, which can be seen as the foundation of following image processing applications.3. Based on the speckle noise modeling for SAR image, a new speckle reduction algorithm using Contourlet transform is proposed. Logarithmic transform is first performed to convert the original multiplicative speckle noise into additive noise. For noise can be effectively separated from real image signal in Contourlet transform domain, a Contourlet based hard thresholding algorithm is then applied. Monte-Carlo method is adopted to estimate the statistics of Contourlet coefficients for speckle noise, thus determining the optimal threshold set. The cycle-spinning technique is utilized to suppress the Gibbs effect. Experimental results show that the proposed Contourlet based algorithm outperforms conventional algorithms in terms of both speckle reduction and edge preservation.4. A novel image fusion algorithm based on local statistics in Contourlet domain is proposed. All fusion operations are performed in Contourlet domain. Based on the tree structure among parent and children coefficients in Contourlet domain, the Contourlet contrast measurement is developed. It is proved to be more suitable for human vision system. Other fusion rules like local energy, weighted average and selection are combined with"region"idea for coefficient selection in the low- and high-pass subbands. The final fusion image is obtained by directly applying inverse Contourlet transform to the fused subbands. Extensive fusion experiments have been made on remote sensing images and medical multimodality images, both visual and quantitative analysis show that comparing with conventional image fusion algorithms, the proposed approach can provide a more satisfactory fusion outcome.5. An example based super-resolution algorithm for digital image using nonsubsampled Contourlet transform (NSCT) is proposed. The input is one or more low resolution images together with a high resolution still image of similar content. For the good properties of shift-invariance, the nonsubsampled Contourlet is utilized to create the training set of transform coefficient patches from the high resolution still image. Low resolution images are first interpolated to the same spatial resolution as the reference still image. Block based motion estimation is then applied inside the complete training set to find the best matching between interpolated frame and reference still image. According to the correspondence between low frequency and high frequency pairs, the missing high frequency information of the input image can be easily learned from the training set. Finally, an inverse Contourlet transform is applied to recover the super-resolved image. Preliminary experimental results show that the proposed super-resolution algorithm outperforms conventional algorithm both in visual quality and the PSNR value.6. Motion estimation (ME) is one of the key techniques in exampled based super resolution and video coding. A novel fast motion estimation algorithm, or the Motion vector field and Direction Adaptive Search technique (MDAS) is proposed. In MDAS, the type of local motion activity and initial search center is first determined according to the motion vector field, different search algorithms are adaptively used according to the motion activity of current block. Two novel search algorithms: Line-Diamond Search (LDS) and Hexagon -Diamond Search (HDS) are proposed, which all have strong directional property, and can obtain faster searching speed. Stop criteria is also set to detect the stationary block, thus terminating current search. Experimental results show that the proposed algorithm provides faster searching speed than other existing fast block-matching algorithm, while the distortion is almost the same as the FS algorithm.
Keywords/Search Tags:Multiscale Geometric Analysis (MGA), Contourlet, Nonsubsampled Contourlet Transform, Speckle noise, Image fusion, Super-resolution, Motion estimation
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