Independent Component Analysis For Image Processing

Independent component analysis (ICA) is a new signal processing technique developed in the last decades and has been used in a broad range of applications. This thesis focuses on the ICA algorithm and its applications in image processing.Based on discussion of ICA basic theory, the thesis analyses the fast fixed point algorithm (FastICA),which is widely applied. Then a modified FastICA algorithm is offered by imposed one-dimensional search on the iterative direction. With the modified algorithm, number of iteration is reduced and convergence performance is improved.In texture image classification, a method to select the texture feature based on independent subspace analysis (ISA) and independent spectral representation is proposed. We can achieve better classification performance by the feature filters comparable to other traditional filter schemes while resulting in considerably smaller filters.In image denoising, two different denoising techniques are developed. The first is based on ICA transform. After obtained the independent components of the corrupted image by ICA, a new compensating operation and an improved shrinkage function are proposed to effectively combat the loss of image details in ordinary soft-thresholding shrinkage. The second is based on BSS. In this method the noisy image is regarded as a mixture by the source image and the noise, and fire-new processing patterns are tried. Based on it, two approaches to separate source image and the noise are developed, which use the theory of reconstructive phase space (RPS) and adding a dummy image as another observation, respectively. The experimental results is satisfied.In image separation, two algorithms are proposed. The first is based on ridgelet transform, it inherits the advantage of wavelet ICA and can improved the separation performance for the mixed images with notable line feature. The method is suitble to separate the mixed images, in which the source images are statistically independent each other. The second is based on complexity pursuit, it describes the process of separation as a process to find out the interesting projective directions, and a fixed point iterated algorithm is developed. It can separate the mixed images successfully, in which the source images are not statistically independent each other.In moving targets detection in the series of images, a modified algorithm of detecting moving targets is presented based on a new gradient leaning algorithm combined Informax and FastICA. It can detect the moving targets in image series accurately and be robust to the noise.