Research On High Performance Image Super Resolution Methods

With the rapid development of computer technology and information processingtechnology, people are increasingly found the importance of improving the quality ofthe information by using existing hardware technology. How to enhance the imageresolution through limit of the inherent information of the image, thereby obtainingmore high-frequency image details, which has important significance researching andapplication value.Based on the abovementioned background, to solve the complexity of thecomputation and unstable reconstruction effect in traditional super resolution methods,one high performance super resolution general-purpose images reconstruction methodis achieved in this paper in the following aspects:A fast image registration method, namely PCA-SIFT-Gaussian is proposed basedon Principal Component Analysis and Gaussian weighted Euclidean distance. Firstly,we introduced the most popular methods of feature extraction and matching. There arefour main steps to extract features by SIFT, Establishment of scale space, detection ofextrema, orientation assignment and generation of SIFT descriptor. To solve the largestorage space and matching time-consuming issues, we introduced3042dimensionalfeature vector surrounding the41×41pixels’ neighborhood. Then, PCA is achieved toreduce the multi-dimensional data instead of old128dimensional feature vectors. Inthe process of feature matching, we used Gaussian weighted Euclidean to replacetraditional Euclidean distance, which will meet the human visual characteristics better.Experimental results show that the proposed algorithm is more effective with fasterapproximately40%speed of feature extraction and matching compared to othermainstream image registration methods. It has higher robustness to Gaussian noise,rotation, and scale changes, affine transformation and illumination variation.An effective super resolution image reconstruction method with faster speed oftraining joint dictionary pair is proposed. This method is based on Lifting WaveletTransform and sparse representation. According to the three main steps of sparse representation, which are image degradation model, local constraints with patchessparse representation, and global constraints, we improved the process of trainingjoint dictionary pair. High-frequency components of the image can be estimated bycalculating only25%pixels in the whole image. That means this presented methodsave up to75%time to train dictionary. On the other hand, PCA-SIFT-Gaussianpresented before is used to achieve the feature extraction factor F instead oftraditional one-dimensional gradient high-pass filter. Experimental results show thatthe proposed algorithm can effectively shorten the dictionary training time by morethan60%, and has higher reconstruction accuracy.A new compressed sensing method, namely Dual-IALM, is proposed to achievematrix completion and matrix recovery simultaneously. After comparing allmainstream methods of matrix completion and matrix recovery, we introduced theAugmented Lagrangian Multiplier method, which has superior convergence andaccuracy and present a new method with this two matrix functions——Dual InexactAugmented Lagrangian Multiplier, abbreviated as Dual-IALM. Experimental resultsshow that this method has fewer iterations times, higher accuracy and strongeranti-noise ability, this method can solve the practical application of image denoisingand image fusion effectively.A high performance image super resolution reconstruction system is achieved formulti-frame sub-pixel displacement low-resolution image sequences captured by oneor more sensors. Super-resolved reconstruction of images can yield poor results in theabsence of extensively-trained related dictionaries. A super-resolution algorithm ispresented which remedies this problem by exploiting recent results from the work onsparse representation and matrix completion. An over-complete dictionary pair istrained using natural image data. Sparse coefficients of low-resolution image patchesare estimated using local prior constraints. In multi-frame images, sparse coefficientsare similar across frames, and the Inexact Augmented Lagrange Multiplier method isemployed to achieve matrix completion and recovery in the process of imposingglobal constraints. Furthermore, we optimize the system with keeping thePCA-SIFT-Gaussian descriptor and introducing Ring-Jacobi ordering to accelerate the singular value decomposition. The final high-resolution image is generated from theoutput low-rank matrix. Experiments reveal that the method yields higher PSNR value(from5.04dB to6.28dB)than other mainstream SR algorithms, produces perceptiblysuperior edges and details, and is more robust to dictionary insufficiency which can becan be applied to the field of remote sensing and machine vision.