Research On Directional Multiresolution Analysis Of Images: Theory And Applications

This thesis is concerned primarily with the research of image analysis based on thedirectional multiresolution transforms and PCNN.The research of directional multiresolution transforms has been one of the hottesttopics of signal processing.There are many methods that retrieve the directionalinformation with the multiresolution analysis.These methods are trying to provide thedirectional representation of high dimension signals and satisfy some properties such asperfect reconstruction,low redundancy and high computing performance.DTCWT,Contourlets,NSCT,PDTDFB are the widely used tools for directional multiresolutiontransforms,but they all suffer from problems for their own structures.PCNN is an outstanding tool for image analysis,which is rotation-invariant,scale-invariant and shift-invariant.PCNN provides different approximate sequences atdifferent scales; therefore it is a multiresolution transform.PCNN checks the edgeinformation directly,which is different from wavelet and other multiscale methods,andsuch edge information can be used in features extraction,image segmentation,targetsrecognition,denoising,enhancement and other applications.The main contents and contributions of the thesis are as follows:Firstly,we present P-Contourlet transform which performs the Contourlettransform on the analytic signals of the original signals and is like the idea of complexwavelet on analytic signal.P-Contourlet is a kind of shift-invariant,high directionalselectivity transform with phase information.Based on the directional selectivity,weclassify P-Contourlet into two categories:P-Contourlet-â… which has one channel andperforms Contourlet transform only once and P-Contourlet-â…¡which has two channelsand performs Contourlet transform on each channel once.P-Contourlet has simpleimplementation structure and lower redundancy.The experiments on textureclassification show that P-Contourlet transform is an efficient tool for image analysis.Secondly,we propose a dual tree Contourlet transform which is motivated by theDTCWT.We study the structure and implementation of PDTDFB and realize that it hassome problems both on filter design and whole system implementation,so we propose a structure named as DTCT which is easy to implementation.The high pass subbandsof Laplacian pyramid are filtered by two parallel trees which are both cascade DFBs toobtain high directional selectivity.Each tree is constructed to be an orthogonal systemand filters of the prime tree and the corresponding filters of dual tree satisfy certainphase constraint conditions,and the whole system is a tight frame.We analyze thespecial structure of DTCT and the properties of the filters and propose a solution fordesigning filters that the system required.DTCT is near shift-invariant and has thesame high directional selectivity as PDTDFB,and has phase information from the dualtree scheme.DTCT has a simple structure and effective implementation compared toPDTDFB.Thirdly,we present an algorithm to pre-classify the texture images based onbinary Fourier spectrum.This algorithm classifies the whole texture images intostructure texture and random texture.The experiments show that the retrieval rate canbe substantially improved in analyzing of structure texture images by using directionalmultiresolution analysis.Lastly,the image analysis methods based on filter banks (such as wavelet,Contourlet,DFBs,etc.) are sensitive to the changes caused by rotation,scale and shift,so we propose an image retrieval algorithm based on PCNN.The features extractedfrom the output of PCNN are invariant to rotation,scale and noise interfering.Theexperiments show the efficiency of our algorithm.