Research On Super-Resolution Reconstruction And Image Enhancement Technique

With the development of multimedia communication and information processing, there is a high demand for high-resolution (HR) images. However, it is hard to get the desired image because of the physical limitations of the image acquisition system. The recorded images are usually degraded, noisy and downsampled. Improving the performance of the hardware is clearly one way to increase the resolution of the acquired images. But this method may not be feasible due to the increased cost. Therefore, alternative methods should be provided in order to increase the spatial resolution. The technique of super-resolution (SR) is developed under this circumstance. The aim of the SR is to improve the resolution using software techniques which makes up the hardware deficiency. Many researchers have devoted themselves to this area and the research of SR will be of great value in a lot of applications such as TV, remote sensing, medical imaging, surveillance system and so on.Super-resolution reconstruction refers to obtain a high resolution image or image sequence by fusing multiple similar but different low-resolution (LR) images and remove the additive noise and optical blur simultaneously. Recently, it has been one of the most active research areas in the world.This dissertation reviews the SR related theory and classical algorithms systematically. Due to the various problems in real applications such as blur estimation, noise, different light conditions and so on, this dissertation presents several super-resolution reconstruction and image enhancement schemes based on vector quantization (VQ), support vector machines (SVM) and manifold learning. In addition, as a preprocessing technique of face recognition, an independent component analysis (ICA) based facial image SR reconstruction approach is proposed and the recognition rate is improved efficiently by this method. Generally, this dissertation consists of the following parts:1. Multi-frame super-resolution reconstruction. Three schemes are presented in this part. Firstly, by combining the L\ norm minimization and stationary wavelet transform (SWT), a robust SWT based SR scheme is proposed to deal with different noise models. This method is also effective to preserve the edges of the image due to the high-frequency information in different directions extracted from the image. Secondly, a kurtosis-based scheme is proposed to address SR image reconstruction in low SNR environments. After the definition of the kurtosis image, its two important properties are analyzed: (a) kurtosis image is free from Gaussian noise; (b) the absolute value of kurtosis image becomes smaller as the image gets smoother. Therefore, the estimated HR image should have the largest absolute local kurtosis. Based on these two characteristics, the HR image is estimated by solving an absolute local kurtosis maximization problem with the constraints that residue of the observed data and the solution are bounded. Lagrange multiplier is applied to solve the combinatorial optimization problem. The proposed method is better than the conventional algorithms in terms of visual inspection under severe noise background and has low computational complexity. Thirdly, a segmentation-based scheme is proposed. In this scheme, the image is divided into various regions by making use of high order statistics. Different regularization terms are applied to homogenous regions and non-homogenous regions according to the segmentation label. Detail information of the reconstructed SR image is well preserved.2. Learning based single-frame super-resolution reconstruction. Two schemes for simultaneous super resolution and image enhancement are presented to solve the illumination problems in SR technique. Based on the manifold learning and self quotient image (SQI), a logarithmic-wavelet transform (Log-WT) is defined for the elimination of lighting effect in the image. After that, illumination-free features are extracted by exploiting Log-WT or SQI. Under the framework of manifold learning and local linear embedding, an initial estimation of high resolution image is obtained based on the assumption that small patches in low resolution space and patches in high resolution space share the similar local manifold structure. Finally the desired HR image is reconstructed by applying the reconstruction constraints in pixel domain. The proposed method simultaneously achieves single-image super-resolution and image enhancement especially shadow removing.3. A blind SR scheme based on vector quantization is proposed. Assume that the blur type is known and blur function is parameterized by one parameter. Based on the VQ technique, the best estimation is found within a set of candidates according to the minimum distortion. Feature extraction by Sobel operator improves the robustness of the method to different types of images. DCT is utilized to reduce the dimension of the vector which leads the low computational complexity. Meanwhile, extension of this method to blind super-resolution image reconstruction is achieved. After blur identification, a super-resolution image is reconstructed from several low-resolution images obtained by different foci.4. A support vector machines based scheme of blind SR image reconstruction is proposed. In this scheme, blur identification problem is solved from the viewpoint of pattern recognition. Edge detection and local variance are used to extract feature vectors which contain the information of blur parameter from training images. Then SVM is used to classify these feature vectors. Finally, the acquired mapping between the vectors and corresponding blur parameters provide the identification of the blur and further estimate the HR image.5. An independent component analysis based face SR scheme is proposed. In this scheme, the independent components (ICs) are obtained by offline training high resolution face images. The prior of ICA coefficients are estimated by performing PCA on training images. Given a LR image, the high resolution image is reconstructed by the linear combination of the ICs where the weight coefficients are obtained by the method of maximum a posteriori (MAP). Experimental results demonstrate that the proposed method is robust to various pose, expressions and lighting conditions. The hallucination results preserve both the global structure and the high spatial-frequency information better such as sharp edges and high contrast. The HR results are then applied to face recognition which improves the recognition rate.In summary, blur identification and image dynamic improvement as well as light conditions and noise removal are investigated in this dissertation. Meanwhile, the new mathematical tools including SVM, manifold learning, and ICA are used in the modeling and solution of the proposed schemes which makes the schemes more useful and flexible. Finally, the problems to be solved related to this research area and future research topics are summarized, furthermore, the prospect of the developing tendency is analyzed as well.