Super Resolution Method For Hyperspectral Image Corrupted By Noise

Hyperspectral image has many applications.Due to various hardware limitations,the acquired hyperspectral images unfortunately have lower spatial resolution than mul-tispectral image or panchromatic image,which influences its real applications.So,it is desirable to develop software techniques to enhance the spatial resolution of hyperspec-tral image.The present methods for hyperspectral image super resolution have less satis-factory performance in robustness against heavy noise.Furthermore,there is not almost literatures about hyperspectral image super resolution in the case of Poisson noise or the mixed Gaussian-Poisson noise.This dissertation forcuscs on hyperspectral image super resolution in various noise.Our major works are summarized as follows:1.A novel method is proposed for hyperspectral image super resolution by a nov-el double regularization unmixing-based technique.The widely used linear observation model is combined with the linear spectral mixture model to form the likelihoods of the observations.The structure properties of the endmember and abundance are introduced as prior information to regularize this ill-posed problem.In detail,the mutual distance be-tween the endmember elements is proposed as the regularization for endmember matrix,and the based on graph laplacian regularization term is presented as the abundance ma-trix regularization.Furthermore,the weight matrix of the graph laplacian is computed by graph learning model.Finally,the designed optimization problem is effectively solved by an alternating direction optimization algorithm.Simulation results illustrate that the pro-posed method has a better performance than several well-known methods,both in terms of quality indexes and reconstruction visual effect.2.A novel method based on sparse representation and nonlocal regularization is p-resented for Poissonian hyperspectral image super resolution.We use dimensionality re-duction technique for hyperspectral image by PCA,which brings the more efficient com-putation and the more accurate estimate since the number of variables to be estimated is significantly reduced.The super resolution scheme is designed as an optimization prob-lem whose cost function consists of the two data-fidelity terms about Poisson distribution,the sparse representation term,and the nonlocal regularization term.The sparse represen-tation term is used for enhancing the quality of sparsity-based signal reconstruction,and the nonlocal regularization term exploits the spatial similarity of hyperspectral image and reduces the artificial information brought by sparse representation.Finally,the designed optimization problem is effectively solved by an alternating direction optimization algo-rithm.Simulation results illustrate that the proposed method has a better performance than several well-known methods,both in terms of quality indexes and reconstruction visual effect.3.A novel method is developed for hyperspectral image super resolution using a Bayesian nonparametric dictionary learning method in the presence of mixed Gaussian-Poisson noise.An optimization model is introduced,including the data-fidelity term cap-turing the statistics of mixed Gaussian-Poisson noise,and a beta process analysis-based sparse representation regularization term.Compared with conventional dictionary learn-ing methods,such as K-SVD and OnLineDL methods,the introduced dictionary learning method is based on a popular beta process factor analysis(BPFA)for an adaptive learning performance.In order to implement the proposed method,we use alternating direction algorithm of multipliers(ADMM)for simultaneous Bayesian nonparametric dictionary learning and image estimation.Variational Bayesian inferring is used for the dictionary learning.Simulation results illustrate that the proposed method has a better performance than several well-known methods in terms of quality indices and reconstruction visual ef-fect.4.A novel hyperspectral image blind super resolution method is proposed in the case of unknown blur kernel.The simultaneous total variation and sparse representation are proposed as abundance regularization terms,while a total variation regularization term is selected for the blur kernel.Because the image and blur kernel are simultaneously estimat-ed,the estimated result error is minimized.Finally,the proposed optimization formulation is effectively solved by block coordinate descent method.Simulation results show the fea-sibility of the proposed method and its advantages over existing approach from two aspects of visual effectiveness and quality indices.