A multi-frame image super-resolution method (original) (raw)
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A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework
Computer Systems Science and Engineering
The prime purpose for the image reconstruction of a multi-frame superresolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images, which is useful in numerous fields. Nevertheless, super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts, which include blurring distortion, noises, and stair-casing effects. Consequently, it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image. In this research work, we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception, which improves human analysis and interpretation processes. Accordingly, we propose a new approach to the image reconstruction of multi-frame super-resolution, so that it is created through the use of the regularization framework. In the proposed approach, the bilateral edge preserving and bilateral total variation regularizations are employed to approximate a high-resolution image generated from a sequence of corresponding images with low-resolution to protect significant features of an image, including sharp image edges and texture details while preventing artifacts. The experimental results of the synthesized image demonstrate that the new proposed approach has improved efficacy both visually and numerically more than other approaches.
A noise-suppressing and edge-preserving multiframe super-resolution image reconstruction method
Signal Processing: Image Communication, 2015
Super-resolution technology is an approach that helps to restore high quality images and videos from degraded ones. The method stems from an ill-posed minimization problem, which is usually solved using the L 2 norm and some regularization techniques. Most of the classical regularizing functionals are based on the Total Variation and the Perona-Malik frameworks, which suffer from undesirable artifacts (blocking and staircasing). To address these problems, we have developed a super-resolution framework that integrates an adaptive diffusion-based regularizer. The model is feature-dependent: linear isotropic in flat regions, a condition that regularizes an image uniformly and removes noise more effectively; and nonlinear anisotropic near boundaries, which helps to preserve important image features, such as edges and contours. Additionally, the new regularizing kernel incorporates a shape-defining parameter that can be automatically updated to ensure convexity and stability of the corresponding energy functional. Comparisons with other methods show that our method is superior and, more importantly, can achieve higher reconstruction factors.
Regularization-based multi-frame super-resolution: A systematic review
Journal of King Saud University - Computer and Information Sciences, 2018
High-resolution is generally required and preferred for producing more detailed information inside the digital images; therefore, this leads to improve the pictorial information for human analysis and interpretation and to enhance the automatic machine perception. However, the real imaging systems may introduce some degradation or artifacts in the digital images. These distortions in the images are caused by a variety of factors such as blurring, aliasing, and noise, which may affect the resolution of imaging systems and produce low-resolution images. Numerous strategies like frequency and spatial domain approaches have been proposed in the literature. Spatial domain approaches are classified as one of the most popular approaches and split into interpolation-based approaches and regularization-based approaches. Nevertheless, these techniques still suffer from artifacts. Regularization-based approaches are a challenging in image super-resolution in the last decade. This paper attempts to investigate the current regularization-based super-resolution approaches which are commonly used for reconstructing the high-resolution image in the last decade. Furthermore, the focus is given on highlighting the strengths and limitations of these approaches aiming at determining their effectiveness and quality in reconstructing high-resolution images.
Iterative Multi-Frame Super-Resolution Image Reconstruction via Variance-Based Fidelity to the Data
Multi-frame Super-Resolution (SR) image reconstruction creates a single High-Resolution (HR) image from a sequence of Low-Resolution (LR) frames. Apart from resolution increment, blurring and noise removal is also achieved. In stochastic regularized methods, the SR problem is formulated by means of two terms, the data-fidelity term and the regularization term. In the present work, a novel estimator named norm has been proposed for utilization in the data-fidelity term. This estimator presents a simple mathematical form based on the variance of the SR estimation error, i.e. on the difference between the estimated LR frame and the corresponding measured LR frame. The introduced norm estimator is combined with the Bilateral Total Variation (BTV) regularization to formulate a novel SR method. The SR performance of the proposed method is directly compared with that of two SR techniques existing in the literature. Experimentation proves that the proposed method outperforms the existing methods.
A new denoising model for multi-frame super-resolution image reconstruction
Signal Processing, 2016
Multi-frame image super-resolution (SR) aims to combine the sub-pixel information from a sequence of low-resolution (LR) images to build a highresolution (HR) one. SR techniques usually suffers from annoying restoration artifacts such as noise, jagged edges, and staircasing effect. In this paper, we aim to increase the performance of SR reconstitution under a variational framework using adaptive diffusion-based regularization term. We propose a new tensor based diffusion regularization that takes the benefit from the diffusion model of Perona-Malik in the flat regions and use a nonlinear tensor derived from the diffusion process of weickert filter near boundaries. Thus, the proposed SR approach can preserve important image features (sharp edges and corners) much better while avoiding artifacts. The synthetic and real experimental results show the effectiveness of the proposed regularisation compared to other methods in both quantitatively and visually.
2016
Abstract: Stochastic regularized methods are quite advantageous in Super-Resolution (SR) image reconstruction problems. In the particular techniques the SR problem is formulated by means of two terms, the data-fidelity term and the regularization term. The present work ex-amines the effect of each one of these terms on the SR reconstruction result with respect to the presence or absence of noise in the Low-Resolution (LR) frames. Experimentation is car-ried out with the widely employed 2L, 1L, Huber and Lorentzian estimators for the data-fidelity term. The Tikhonov and Bilateral (B) Total Variation (TV) techniques are employed for the regularization term. The extracted conclusions can, in practice, help to select an effec-tive SR method for a given sequence of LR frames. Thus, in case that the potential methods present common data-fidelity or regularization term, and frames are noiseless, the method which employs the most robust regularization or data-fidelity term should be used. O...
Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters
Journal of Electrical and Computer Engineering, 2021
The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This prep...
Information Fusion, 2012
Stochastic regularized methods are quite advantageous in super-resolution (SR) image reconstruction problems. In the particular techniques, the SR problem is formulated by means of two terms, the datafidelity term and the regularization term. The present work examines the effect of each one of these terms on the SR reconstruction result with respect to the presence or absence of noise in the low-resolution (LR) frames. Experimentation is carried out with the widely employed L 2 , L 1 , Huber and Lorentzian estimators for the data-fidelity term. The Tikhonov and Bilateral (B) Total Variation (TV) techniques are employed for the regularization term. The extracted conclusions can, in practice, help to select an effective SR method for a given sequence of LR frames. Thus, in case that the potential methods present common data-fidelity or regularization term, and frames are noiseless, the method which employs the most robust regularization or data-fidelity term should be used. Otherwise, experimental conclusions regarding performance ranking vary with the presence of noise in frames, the noise model as well as the difference in robustness of efficiency between the rival terms. Estimators employed for the data-fidelity term or regularizations stand for the rival terms.
Multi-Frame Super-Resolution Image Reconstruction Employing the Novel Estimator L1inv-Norm
2012
In multi-frame Super-Resolution (SR) image reconstruction a single High-Resolution (HR) image is created from a sequence of Low-Resolution (LR) frames. This work considers stochastic regularized multi-frame SR image reconstruction from the data-fidelity point of view. In fact, a novel estimator named inv L1 norm is proposed for assuring fidelity to the measured data. This estimator presents the hybrid form of both 1 L error norm and logarithm ln. The introduced inv L1 norm is combined with the Bilateral Total Variation (BTV) regularization. The proposed SR method is directly compared with an existing SR method which employs the Lorentzian estimator in combination with the BTV regularizer. The experimental results prove that the proposed technique predominates over the existing technique. Index Terms-super-resolution, data-fidelity, hybrid form, 1 L estimator, logarithm ln 1. INTRODUCTION Multi-frame Super-Resolution (SR) image reconstruction techniques can serve for processing a number of Low-Resolution (LR) images of a scene to obtain a High-Resolution (HR) image. Basically, in SR reconstruction changes in the LR images caused by blur and motion provide additional data that can be employed to reconstruct the HR image from the observed LR images. SR techniques are applicable in fields like surveillance, remote sensing, medical and nano-imaging. Concerning stochastic regularized SR image reconstruction [1-2], in the literature there have been presented several methods which employ different error norms or estimators [3] for the data-fidelity term. In fact, certain error norms distinguish between usable and not usable measurements. The latter are regarded outliers and treated specially. Thus, the specific estimators present outliers rejection threshold. Such estimators having already been employed for the SR reconstruction task are the Huber, Lorentzian, Tukey, Hampel, Andrew's sine and Gaussian-weighted 2 L [3-8]. This work presents a novel Bayesian regularized SR method. Actually, a new re-descending estimator [3] named
A Mixed Non‐local Prior Model for Image Super‐resolution Reconstruction
Chinese Journal of Electronics, 2017
Generating high-resolution image from a set of degraded low-resolution images is a challenge problem in image processing. Due to the ill-posed nature of Super-resolution (SR), it is necessary to find an effective image prior model to make it well-posed. For this purpose, we propose a mixed non-local prior model by adaptively combining the non-local total variation and non-local H1 models, and establish a multi-frame SR method based on this mixed non-local prior model. The unknown Highresolution (HR) image, motion parameters and hyperparameters related to the new prior model and noise statistics are determined automatically, resulting in an unsupervised super-resolution method. Extensive experiments demonstrate the effectiveness of the proposed SR method, which can not only preserve image details better but also suppress noise better.