Research On Super-resolution Reconstruction Of Video And Image Sequences

With the rapid improvements of video and image processing technologies inrecent years, the demand for high-quality video and image sequences grows fast. Ahigh-quality image always contains further detailed information of targets, and it is ofgreat value for analysis and post-process. But in some application areas, under limitedoptical elements, processors, channel bandwidths or storage capacities, the imageresolution is always unable to meet our needs. Furthermore, it is impossible or hard tobreak the limitations. So, how to enhance the spatial resolution of video and imagesequences under these limitations becomes a research hotspot.Super-resolution image reconstruction technique has been proved to be anefficient technique to solve the above problems. It fuses complementary informationof several low resolution images by signal processing methods to get a highresolution image. It can enhance the spatial resolution of images effectively withoutany upgrade of current equipments. This technique provides us with an efficientapproach to obtain high-quality videos and images subject to the constraints of opticaldevices, processors or communication channels. Thereby, it is worthy of notice bothfor academic studies and applications, and it permits widespread deployment.The dissertation investigates several key issues of super-resolution of image andimage sequences including registration estimation, and regularization based imagereconstruction, and has obtained many results.The main contributions and innovation points of the dissertation are as follows:1)The Cramer-Rao bound for a general parametric registration with specialattention to two cases: translational and2D projective registration is analyzed. Biasesof the gradient-based estimator are then derived. This dissertation corrects the shift bias in an iterative manner and shows that the iterative gradient-based estimator isoptimal. presents two techniques for improving registration of (under-sampled)image sequences. The first one is Bundle adjustment, for example, reduces thevariance of registration by enforcing consistent flows from SINGLE image to anothervia any of the intermediate image routes. Registration on a high-resolution\fusion"image is another technique that improves multi-frame registration.2)In some applications, it needs to enhance the resolution of a single frame. a novelimage interpolation algorithm that uses the new contourlet transform to improve theregularity of object boundaries in the generated images is proposed. Assumes thegiven low-resolution image is the lowpass subband of an wavelet transform of theunknown high-resolution image, while all the coefficients in the highpass subbandshave been discarded. Firstly, By using a simple wavelet-based linear interpolationscheme as our initial estimate. We then attempt to improve the quality ofinterpolation, particularly in regions containing edges and contours, by iterativelyenforcing the observation constraint as well as the sparseness constraint.Experimental results show that our new algorithm significantly outperforms linearinterpolation in subjective quality, and in most cases, in terms of PSNR as well.3)Some limitations of conventional super-resolution methods are analyzed. To solvethese problems, proposes a general cost function that consists of weighted L1-andL2-norms considering the SR noise model where the weights are generated from theerror of registration and penalize parts that are inaccurately registered. Both thesuper-resolved images and blurring operators are jointly estimated. The objective andsubjective results are shown to demonstrate the effectiveness of the proposedalgorithm.4)A new class of algorithms for super-resolution of image sequences is proposed.This class of algorithms estimates simultaneously all frames of a sequence byemploying an iterative minimization of a regularized cost function. Similarly to othersuper-resolution techniques, the proposed approach exploits the correlation amongthe frames of the sequence. This correlated information helps to improve theresolution of the captured images. By employing the motion information only in theprior term of the cost function, the proposed method achieves a better fidelity andmore robust performance. An implementation utilizing Conjugated Gradient, withfast convergence, is presented.The proposed method is compared with other classicalmethods in the literature and the experimental results clearly indicated that theproposed method produces images with higher quality and lower computationalcost. Besides, the proposed method, with Huber norm, is very robust to outliers andprovides edge-preservation.