Research Of Fast Single Image Super-resolution And Pattern Classification

With the information increases continuously,high speed of traditional signal sampling technologies which based on the conditions of signal-compressed and sparseness of the transforming domain,can easily cause the verbose messages when dealing with the sampling.Candes and others provide that compressed sensing theory which lies on the Functional analysis and Approximation theory,is sampling compressed techniques of low sampling rate.It's well known for the unique compressed structure and relatively low sampling speed within several years,which solve the issues in traditional methods.Therefore,this thesis exploits compressed sensing theory to improve traditional image processing approaches and mainly proposes a novel algorithm for fast single image super-resolution based on self-example learning and sparse representation.We propose an efficient implementation based on the K-singular value decomposition(SVD)algorithm,where we replace the exact SVD computation with a much faster approximation,and we employ the straightforward orthogonal matching pursuit algorithm,which is more suitable for our proposed self-example-learning-based sparse reconstruction with far fewer signals.The patches used for dictionary learning are efficiently sampled from the low-resolution input image itself using our proposed sample mean square error strategy,without an external training set containing a large collection of high-resolution images.Moreover,the l0-optimization-based criterion,which is much faster than l1-optimization-based relaxation,is applied to both the dictionary learning and reconstruction phases.Compared with other super-resolution reconstruction methods,our low-dimensional dictionary is a more compact representation of patch pairs and it is capable of learning global and local information jointly,thereby reducing the computational cost substantially.Our algorithm can generate high-resolution images that have similar quality to other methods but with an increase in the computational efficiency greater than hundredfold.Second,remote sensing land-use scene classification has a wide range of applications including forestry,urban-growth analysis,and weather forecasting.Therefore,this thesis also presents an effective image representation method,Gabor-filtering-based completed local binary patterns(GCLBP),for land-use scene classification.It employs the multi-orientation Gabor filters to capture the global texture information from an input image.Then,a local operator called CLBP is utilized to extract the local texture features,such as edges and corners,from the Gabor feature images and the input image.The resulted CLBP histogram features are concatenated to represent an input image.Experimental results on two datasets demonstrate that the proposed method is superior to the existing methods for land-use scene classification.The achievements above can effectively and quickly solve the pixel information shortage problem due to low-resolution images and remote sensing land-use scene classification respectively which provide a useful reference for the field of image super-resolution reconstruction and remote sensing image pattern classification.