Research On The Algorithm Of Image Fusion And Super Resolution Reconstruction Based On Compressive Sensing

The traditional algorithm of image fusion and super-resolution reconstruction use the all pixel information of source image to enhance the image characteristics,the algorithm has a higher complexity degree of time and space.The compressed sensing theory makes use of measurement matrix to reduce the dimension of the signal.It can reduce the sampling frequency and reduce the requirement of data transmission,processing and storage.The algorithm of image fusion and super-resolution reconstruction based on compressive sensing theory are beneficial to improve the spatial resolution of image degradation.It is beneficial to make up for the lack of spatial resolution of the original image.The algorithm can obtain more image features and detail parameters,which is important in image feature extraction,classification and recognition.The dissertation analyzed the algorithm of image fusion and super-resolution reconstruction based on compressed sensing,and applied the algorithm to realize the remote sensing image fusion and super-resolution reconstruction.The algorithm has achieved good application effect.Firstly,the dissertation analyzed the compressed sensing theory,and put forward the improved algorithm of image sparse based on the K-SVD,and proposed the improved algorithm of image measurement and reconstruction algorithm based on the improved Hadamard matrix.Secondly,the dissertation analysised the algorithm of image fusion based on compressive sensing theory,and proposed the image fusion algorithm based on the wavelet transform and the Fourier random measurement matrix,and put forward the image fusion algorithm based on the improved K-SVD and Hadamard measurement matrix.Finally,the dissertation realized the super-resolution reconstruction algorithm of the single image based on the learning-dictionary and compressive sensing.The main contributions and innovation points as follows:(1)The dissertation put forward the improved K-SVD algorithm of image sparse representation.The dissertation studied the algorithm of image sparse representation,and put forward the improved algorithm based on the K-SVD.The improved algorithm obtained the multi-component dictionary based on the image geometric features,such as smooth,edge and texture structure,etc.The improved K-SVD algorithm breaks the restriction condition of the orthogonal basis,and uses different orthogonal bases to form a frame,and represented the image by a super-complete basis.The Dictionaries are more compact based on the improved algorithm,and the sparse factor is smaller compared with the traditional K-SVD algorithm,which has better effect on the sparse representation of multiple features.(2)The dissertation put forward the improved algorithm of Hadamard matrix,and realized the image measurement and reconstruction based on the improved Hadamard.The dissertation studied the construction and optimization of adaptive measurement matrix,and put forward the improved Hadamard measurement matrix.The dissertation analyzed the optimization algorithm of matrix.The improved Hadamard matrix has fewer elements of independent random variations,and the structure of the measurement matrix can be achieved by using the fast cyclic shift transform.At the same time,the structure of the measurement matrix has better sparsity,which avoids the existence of the matrix of the measurement matrix.In the reconstruction algorithm OMP,the improved Hadamard measurement matrix is used to obtain a better reconstruction effect.The reconstruction algorithm obtains satisfactory results in the aspects of PSNR value and the execution time of the algorithm.(3)The dissertation put forward the fusion algorithm based on improved K-SVD and Hadamard measurement matrix.The dissertation analyzed the image fusion method based on the theory of compressed sensing.The dissertation analyzed the image fusion algorithm based on wavelet transform and FuLiye random measurement matrix,and put forward the image fusion algorithm based on the improved K-SVD and Hadamard measurement matrix.In the image fusion algorithm based on the improved K-SVD and Hadamard measurement matrix,the algorithm firstly used the improve K-SVD algorithm to represent the sparsed image,and then used the improved Hadamard measurement matrix to reduce the dimension of the measurement,at last,the fusion image is reconstructed by using the weighted fusion rule method in the compressed sensing domain.The experimental results show that the fusion algorithm is effective in reducing the number of pixels in the fusion algorithm and improving the time and space complexity of the fusion algorithm.(4)The dissertation put forward the algorithm of super-resolution reconstruction based on the compressed sensing and learning dictionary for single image.The dissertation analyzed the super-resolution reconstruction algorithm based on the compression sensing and learning dictionary,this dissertation analyzed the image self similarity,and analyzed the training algorithm of learning dictionary.In the algorithm of super resolution reconstruction,the algorithm introduced the low-pass filter,and using the improved local-Hadamard matrix as the measurement matrix,which ensure that the algorithm meets the RIP criteria.Finally,the dissertation realized the single image super resolution reconstruction with the iterative algorithm.Experiments show that the reconstructed images have good visual effect,and the PSNR value is improved compared with the bilinear interpolation and SRSR algorithm.(5)The dissertation realized the fusion and super resolution reconstruction of remote sensing images.The dissertation realized the fusion of visible image and infrared remote sensing images using the proposed algorithm,and realized the fusion of multi-spectral remote sensing image and visible remote sensing image.At the same time,the super resolution reconstruction of remote sensing image is achieved by using the proposed algorithm.The algorithm has achieved good results of the remote sensing image fusion and super resolution reconstruction.