A practical approach to superresolution

ICTACT Journal on Image and Video Processing, 2019

The image processing field is quite advance now a days. Many important developments have taken place over the last three or four decades. Super Resolution is really very important subject for Image Processing. Super resolution describes increasing the resolution of an image using various algorithms and construct a high resolution image from one or more low resolution images. This paper reviews various superresolution technique with their advantages and disadvantages. Finally presented challenge issues and future research directions for super resolution.

International Journal of Computer Applications, 2014

Today, in many applications of Machine vision, image Super-Resolution is preferred. Super-Resolution is estimation of a high-resolution image from an image or several low resolution images. Popular techniques in the field of enhancing images can be used to remove noise or blurring. In this paper, an overview of super resolution methods has been presented. Types of resolution methods have been used so far can be divided into three groups as frequency-domain methods, spatial domain methods and techniques can be classified as the wavelet domain. Super-resolution methods in different domains have different characteristics and comparison between these methods is usually done using a special index in one domain. In this paper, we will introduce these indexes and review best techniques used in all three domains.

Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. Super-resolution imaging (SR) is a class of techniques that enhance the resolution of an imaging system. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. Super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy.

Image Super-Resolution (SR) is a technique to reconstruct High-Resolution (HR) images using one or more Low-Resolution (LR) images. This paper brings about a detailed study on image Super-Resolution Techniques. Different categories of image Resolution and the process, Image Super-Resolution are well described. A detailed description of different SR approaches is given and certain relevant SR methods are explained. This paper also gives a qualitative and quantitative performance evaluation and comparison of various SR methods.

In review paper [4], authors Huahua Chen, Baolin Jiang, Weiqiang Chen have demonstrated that a super-resolution based on image patches structure. This method have not only has better quality but less consuming time than Yang [11] method.

2003

2016

Image reconstruction techniques are used to create a two dimensional and three dimensional images. For image reconstruction different methods are used such as back projection filters. Image reconstruction is commonly referd to as restoration of missing parts. Super-resolution technique aims to increase the resolution of the limits of the original image or video. It is used to extract the lost details of an image when it was up scaled. Interpolation based SR, Example based SR and Multi image based SR are the main techniques for reconstructing a super resolution image from LR image. Resolution refers to denote the number of pixels in an image and it is measured in pixel Per Inch (PPI). SR technique reduces the image’s blurring and used in many image processing applications. SR technique applied in an improvement of test images, compressed video and image enhancement, medical imaging process and satellite and aerial imaging. SR is a badly postured issue on the grounds that every LR pix...

IEEE Signal Processing Magazine, 2003

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.

proceedings of SPIE …, 2003

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001

Super-resolution is a technique that produces higher resolution images from low resolution images (LRIs). In practice, people have found that the improvement in resolution is limited. The aim of this paper is to address the problem "do fundamental limits exist for super-resolution?". Specifically, this paper provides explicit limits for a major class of super-resolution algorithms, called the reconstructionbased algorithms, under both real and synthetic conditions. Our analysis is based on perturbation theory of linear systems. We also show that a sufficient number of LRIs can be determined to reach the limit. Both real and synthetic experiments are carried out to verify our analysis.

2010

The subject of extracting particular high-resolution data from low-resolution images is one of the most important digital image processing applications in recent years, attracting much research. This paper shows how to improve the resolution of real images when given image is in the degraded form. In the superresolution restoration problem, an improved resolution image is restored from several geometrically warped, blurred, and noisy and downsampled measured images. To obtain this result the use an iterative nonlinear restoration blind deconvolution maximum likely-hood algorithm imposing the low frequencies complete data of the original low-resolution image and the high-resolution data present only in a fraction of the image which suppresses the noise amplification and avoid the ringing in deblurred image. Our results show that a high resolution real image derived from superresolution methods enhance spatial resolution and provides substantially more image details.

IEEE, 2018

One of the primary measurements of image quality is image resolution. High-resolution images are often required and desired for most of applications as they embody supplementary information. However, the best utilization of image sensors and optical technologies to increase the image pixel density is usually restrictive and overpriced. Therefore, the effective use of image processing techniques for acquiring a high-resolution image generated from low-resolution images is an inexpensive and powerful solution. This kind of image improvement is named image super-resolution. This paper undertakes to investigate the current super-resolution approaches adopted to generate a highresolution image. Furthermore, it highlights the strengths and the limitations of these approaches. More to the point, several image quality metrics are discussed to measure the similarity between the reconstructed image and the original image.

Signal Processing, 2010

Multi-frame image super-resolution (SR) aims to utilize information from a set of lowresolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularizationbased schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction.

IEEE Transactions on Image Processing, 2001

Superresolution reconstruction produces a high-resolution image from a set of low-resolution images. Previous iterative methods for superresolution [9], [11], [18], [27], [30] had not adequately addressed the computational and numerical issues for this ill-conditioned and typically underdetermined large scale problem.

ijcset.com

I. INTRODUCTION Super-resolution image restoration refers to the image processing algorithm which produces high quality, superresolution (SR) images from a set of low-quality, low resolution (LR) images. It is generally regarded as consisting of three steps – image registration, ...