A Review on Super Resolution Techniques

Improving image quality has always been an issue of image technology. Enhancing the quality of image is a continuous ongoing process. For some applications it becomes essential to have best quality of image such as in forensic department, where in order to retrieve maximum possible information image has to be enlarged in terms of size. For example sometimes in forensic investigations either criminal face or in video surveillance a licences number plate, increased image size helps to extract minute information embedded in the image. During this exercise beyond a certain limit, the enlarged image results in a blurred picture. Most possible cause of this problem is the hardware limitation, primarily which includes the sampling rate of a Charge- Coupled Device [CCD]. Especially this becomes very critical while capturing a high speed moving object. In such case pre/post-image processing is required which in turn restores the High resolution (HR) / Super resolution (SR) image(s) from the captured low resolution (LR) image(s). Such obtained high quality images have also a concern in satellite imaging, medical science, high Definition Television (HD TV), etc. There are various techniques to attain an image with higher resolution. In this paper some of the approaches of super resolution are discussed.

A brief introduction to spatial-domain Super-Resolution methods, i.e. spatial resolution enhancement methods that create one high-resolution image from a series of low-resolution images shifted by a sub-pixel distance, is given. An improvement applicable to some of existing Super-Resolution methods is presented. Principles of digital photography processing techniques are exploited in order to reduce error in the Super-Resolution process. Enhanced registration method applicable to full color images is proposed and results of its hardware implementation are presented.

International Journal of Trend in Scientific Research and Development

In this paper, a detailed survey study on single image super-resolution (SR) has been presented, which aims at recovering a high-resolution (HR) image from a given low-resolution (LR) one. It is always the research emphasis because of the requirement of higher definition video displaying, such as the new generation of Ultra High Definition (UHD) TVs. Super-resolution (SR) is a popular topic of image processing that focuses on the enhancement of image resolution. In general, SR takes one or several low resolution (LR) images as input and maps output images with high resolution (HR), which has been widely applied in remote sensing, medical imaging, biometric identification.

2015

Resolution plays a major role for interpretation and analysis of an image. Super Resolution is a technique to enhance the resolution of an image from single or multiple low resolved images, which gives detailed information present in an image. In this paper, we describe several methods for Super Resolution (SR) that enhances the quality of an image. Mainly the methods are divided into frequency domain and spatial domains. Here, we stated comparison of different approaches, challenges and issues for SR and applications of SR in practical world e.g. in medical imaging, satellite imaging, and forensics. We have approached SR using learning based techniques. We present a novel self-learning approach with multiple kernel learning for adaptive kernel selection for SR. The Multiple Kernel Learning is theoretically and technically very attractive, because it learns the kernel weights and the classifier simultaneously based on the margin criterion. With theoretical supports of kernel matching search method and Optimization approach (Gradient) are proposed our SR framework learns and selects the optimal Kernel ridge regression model when producing an SR image, which results in the minimum SR reconstruction error.

INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH (IJEDR) (ISSN:2321-9939), 2014

The Image Quality can be most often measured in terms of Resolution. The clarity of the Image can be determined by Resolution which means higher the resolution, the image can be more clearer which is most often required in most of the applications. It can be achieved by use of Good Sensors ad optics, but it can be very expensive and also limit way of pixel density within Image. Instead of that we can use image processing methods to obtain High resolution image from low resolution image which can very effective and cheap solution. This Kind of Image Enhancement is called Super Resolution Image Reconstruction. This paper focuses on the definition, implementation and analysis on well-known techniques of super resolution. The comparison and analysis are the main concerns to understand the improvements of the super resolution methods over single frame interpolation techniques. In addition, the comparison also gives us an insight to the practical uses of super resolution methods. As a result of the analysis, the critical examination of the techniques and their performance evaluation are achieved. Super Resolution is particularly useful in forensic imaging, where the extraction of minute details in an image can help to solve a major crime cases. Super-resolution image restoration has been one of the most important research areas in recent years which goals to obtain a high resolution (HR) image from low resolutions (LR) blurred, noisy, under sampled and displaced image.

Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may exhibit insufficient spatial and temporal resolution. Super-Resolution (SR) image reconstruction is a promising technique of digital imaging which attempts to reconstruct High Resolution (HR) imagery by fusing the partial information contained within a number of under-sampled low-resolution (LR) images of that scene during the image reconstruction process. Super-resolution image reconstruction involves up-sampling of under-sampled images thereby filtering out distortions such as noise and blur. In comparison to various image enhancement techniques, super-resolution image reconstruction technique not only improves the quality of under-sa...

The majority of applications requiring high resolution images to derive and analyze data accurately and easily. Image super resolution is playing an effective role in those applications. Image super resolution is the process of producing high resolution image from low resolution image. In this paper, we study various image super resolution techniques with respect to the quality of results and processing time. This comparative study introduces a comparison between four algorithms of single image super-resolution. For fair comparison, the compared algorithms are tested on the same dataset and same platform to show the major advantages of one over the others.

High resolution (HR) images have great importance in many areas, such as astronomy, medical, video surveillance, etc. These HR images give some additional details which are of great significance for analysis in various applications. This paper investigates mainly on various methods of super resolution that are existing, and putting it all together for a literature survey. Scope of this study mainly focuses on the different available techniques of image processing to get high resolution images.

2013

Multi-frame Image Super-Resolution is to generate the high-resolution (HR) image from multiplelow-resolution images perspectives of a same scene and also increase spatial resolution by fusing information. In that first, Image registration which is most important part of multi-frame Superresolution, they have give accurate alignment using the registration parameter. In this paper, we are going to review the different image registration methods and compare all the methods. Then next using various Super resolution methods which is generate high-resolution(HR) image from one or more low resolution images and lastly different image quality metrics reviewed as measure the original image and reconstructed image.