Fast Single Image Super-Resolution via Self-Example Learning and Sparse Representation (original) (raw)
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Single Image Super-Resolution through Sparse Representation via Coupled Dictionary learning
International Journal of Electronics and Telecommunications, 2020
Abstract- Single Image Super-Resolution (SISR) through sparse representation has received much attention in the past decade due to significant development in sparse coding algorithms. However, recovering high-frequency textures is a major bottleneck of existing SISR algorithms. Considering this, dictionary learning approaches are to be utilized to extract high-frequency textures which improve SISR performance significantly. In this paper, we have proposed the SISR algorithm through sparse representation which involves learning of Low Resolution (LR) and High Resolution (HR) dictionaries simultaneously from the training set. The idea of training coupled dictionaries preserves correlation between HR and LR patches to enhance the Super-resolved image. To demonstrate the effectiveness of the proposed algorithm, a visual comparison is made with popular SISR algorithms and also quantified through quality metrics. The proposed algorithm outperforms compared to existing SISR algorithms qua...
Bio-Medical Materials and Engineering, 2015
Image super-resolution (SR) plays a vital role in medical imaging that allows a more efficient and effective diagnosis process. Usually, diagnosing is difficult and inaccurate from low-resolution (LR) and noisy images. Resolution enhancement through conventional interpolation methods strongly affects the precision of consequent processing steps, such as segmentation and registration. Therefore, we propose an efficient sparse coded image SR reconstruction technique using a trained dictionary. We apply a simple and efficient regularized version of orthogonal matching pursuit (ROMP) to seek the coefficients of sparse representation. ROMP has the transparency and greediness of OMP and the robustness of the L 1minization that enhance the dictionary learning process to capture feature descriptors such as oriented edges and contours from complex images like brain MRIs. The sparse coding part of the K-SVD dictionary training procedure is modified by substituting OMP with ROMP. The dictionary update stage allows simultaneously updating an arbitrary number of atoms and vectors of sparse coefficients. In SR reconstruction, ROMP is used to determine the vector of sparse coefficients for the underlying patch. The recovered representations are then applied to the trained dictionary, and finally, an optimization leads to high-resolution output of high-quality. Experimental results demonstrate that the super-resolution reconstruction quality of the proposed scheme is comparatively better than other state-of-the-art schemes.
Super Resolution and Denoising of Images via Dictionary Learning
Improving the quality of image has always been an issue of image technology. Enhancing the quality of image is a continuous ongoing process. To ensure the quality of image in image processing noise estimation and removal are very important step before analysis or using image. An example-based method for super-resolution and denoising of images is proposed. The objective is to estimate a high-resolution image from a noisy low-resolution image, with the help of a given database of high and low-resolution image patch pairs. This method uses a redundant dictionary learning to reconstruct the HR image. The redundant dictionary is trained by K-SVD algorithm which is an iterative method that alternates between sparse coding of the examples based on the current dictionary, and a process of updating the dictionary atoms to better fit the data. Denoising and super-resolution of images in this paper is performed on each image patch. In this paper K-means Singular Value Decomposition (K-SVD) and Iterative Back Projection methods are proposed for denoising and Super Resolution of images. These algorithms significantly improve the resolution and eliminate the blur and noise associated with low resolution images, when compared with the other existing methods.
Computers & Electrical Engineering, 2017
Super-resolution obtains a new high resolution image from single or multiple lowresolution images for the same scene. Recently compressive sensing has been successfully used in signal recovery. This paper investigates the potential reduction in execution time by selecting tasks that can be parallelized using general purpose computing on graphics processing units (GPGPU) and Compute Unified Device Architecture (CUDA). The self example based super-resolution method via sparse representation and morphological component analysis is proposed for satellite images. Orthogonal Matching Pursuit (OMP) is used in the high resolution image reconstruction phase. The complexity of each module in the OMP algorithm is analyzed and its bottlenecks are identified at the projection module and the least squares module. The projection module is accelerated by adopting a GPU tiled matrix vector multiplication. To speedup the least square module, a GPU implementation of the Jordan matrix inverse algorithm is adopted. Different experiments have been carried out on synthetic and satellite images. Extensive experimental comparisons were conducted with state-of-the-art super-resolution algorithms to validate the effectiveness of the proposed approach. The proposed GPU implementation for OMP is tested on NVS 5200M GPU on Intel ® Core(TM) i7 CPU. The GPU implementation accelerates the speedup compared to the CPU sequential implementation from 20 × for small images to more than 40 × for large image sizes.
Sparse Representation Based Single Image Dictionary Construction For Image Super Resolution
2015
Sparse representation-linear combination of very few elements which are represented in vector format called as atoms. Dictionary-specific data set is formed to reconstruct the signal. Generally dictionary can be trained in two ways (i) Based on sparse mathematical model (ii) learning a dictionary based on training set or external images. In this paper a new Super Resolution (SR) approach is employed for underwater side scan sonar image. The SR technique exploits dictionary based sparse representation model. In order to extemporaneously construct an over-complete dictionary from a single image a new algorithm is proposed. Proposed approach improves performance through joint combinatorial optimization of sparse coefficients and Dictionary. In proposed super resolution technique efficiency is measured using various performance metrics like peak signal to noise ratio (PSNR) and structural similarity index measurement (SSIM). Proposed technique of super resolution further reduces the computational time.
Super resolution image reconstruction via dual dictionary learning in sparse environment
International Journal of Electrical and Computer Engineering (IJECE)
Patch-based super resolution is a method in which spatial features from a low-resolution (LR) patch are used as references for the reconstruction of high-resolution (HR) image patches. Sparse representation for each patch is extracted. These coefficients obtained are used to recover HR patch. One dictionary is trained for LR image patches, and another dictionary is trained for HR image patches and both dictionaries are jointly trained. In the proposed method, high frequency (HF) details required are treated as combination of main high frequency (MHF) and residual high frequency (RHF). Hence, dual-dictionary learning is proposed for main dictionary learning and residual dictionary learning. This is required to recover MHF and RHF respectively for recovering finer image details. Experiments are carried out to test the proposed technique on different test images. The results illustrate the efficacy of the proposed algorithm.
Single image super resolution using compressive K-SVD and fusion of sparse approximation algorithms
Super Resolution based on Compressed Sensing (CS) considers low resolution (LR) image patch as the compressive measurement of its corresponding high resolution (HR) patch. In this paper we propose a single image super resolution scheme with compressive K-SVD algorithm(CKSVD) for dictionary learning incorporating fusion of sparse approximation algorithms to produce better results. The CKSVD algorithm is able to learn a dictionary on a set of training signals using only compressive sensing measurements of them. In the fusion based scheme used for sparse approximation, several CS reconstruction algorithms participate and they are executed in parallel, independently. The final estimate of the underlying sparse signal is derived by fusing the estimates obtained from the participating algorithms. The experimental results show that the proposed scheme demands fewer CS measurements for creating better quality super resolved images in terms of both PSNR and visual perception.
Super-resolution image reconstruction using sparse parameter dictionary framework
Super-resolution (SR) image reconstruction is the signal processing technique of fusing many low resolution images into a single higher resolution image. A sparse parameter dictionary framework for super-resolution image reconstruction is proposed, which amalgamates the feature patches of high resolution and low-resolution images using sparse parameter dictionary coding. This technique fabricates a sparse connection between middle-frequency and high-frequency image elements and comprehends concurrently match searching and optimization methods. Comparison with sparse coding method shows sparse parameter dictionary is more dense and efficient. Sparse Kernel-Single Value Decomposition algorithm is applied for optimization to fasten the sparse coding process. Few experiments with real images depict that sparse parameter dictionary coding surpasses all other learning-based super-resolution algorithms in terms of PSNR.
Super-Resolution Image Reconstruction with Improved Sparse Representation
International Journal of Software Engineering and Its Applications
In this paper, we present a new approach to reconstruct a high resolution (HR) image from a low resolution (LR) input image based on a two dimensional (2D) sparse method. The new method consists of three phases. Firstly, the nonlinear feature of the input LR image is divided into the linear subspace, and then LR-HR dictionaries are learned to reduce the blurred artifacts of the image. Secondly, 2D sparse representation and selfsimilarity are developed to strengthen and enhance the image structure. Finally, the final HR image is achieved by reconstruction of all HR patches. Simulation results demonstrated that our proposed method achieved superior results on real images, and shows various improvements in terms of PSNR and SSIM values as compared with some other competent methods.
Single image super-resolution by directionally structured coupled dictionary learning
EURASIP Journal on Image and Video Processing, 2016
In this paper, a new algorithm is proposed based on coupled dictionary learning with mapping function for the problem of single-image super-resolution. Dictionaries are designed for a set of clustered data. Data is classified into directional clusters by correlation criterion. The training data is structured into nine clusters based on correlation between the data patches and already developed directional templates. The invariance of the sparse representations is assumed for the task of super-resolution. For each cluster, a pair of high-resolution and low-resolution dictionaries are designed along with their mapping functions. This coupled dictionary learning with a mapping function helps in strengthening the invariance of sparse representation coefficients for different resolution levels. During the reconstruction phase, for a given low-resolution patch a set of directional clustered dictionaries are used, and the cluster is selected which gives the least sparse representation error. Then, a pair of dictionaries with mapping functions of that cluster are used for the high-resolution patch approximation. The proposed algorithm is compared with earlier work including the currently top-ranked super-resolution algorithm. By the proposed mechanism, the recovery of directional fine features becomes prominent.