A Review on Super Resolution Technique (original) (raw)

A Review on Super Resolution Techniques

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.

A STUDY ON IMAGE SUPER-RESOLUTION TECHNIQUES

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.

A Survey on Image Super- Resolution Techniques for Image Reconstruction

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...

Image Super Resolution-A Survey

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 SURVEY ON SUPER RESOLUTION TECHNIQUES

The main aim of super resolution image is to reconstruct a high-resolution(HR) image from low resolution images. Nowadays super resolution images are used in many applications such as, satellite image, medical image etc. In resolution enhancement of images the main loss is high frequency contents (edges) of the image. Hence in order to enhance the quality of image, preserving the edges is necessary. This paper compares various image resolution enhancement techniques such as discrete wavelet transform (DWT), stationary wavelet transform(SWT), dual tree complex wavelet transform (DT-CWT), wavelet zero padding (WZP), cycle spinning (CS), Vector-Valued Image Regularization with Partial Differential Equations (VVIR-PDE), Inter Sub band Correlation Technique (ISC).

Literature Review on Single Image Super Resolution

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.

Different Implemented Techniques of Super Resolution Imaging

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.

A Survey on Image Reconstruction Using Super Resolution

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...

ANALYSIS OF SINGLE FRAME SUPER RESOLUTION METHODS

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.

SINGLE IMAGE SUPER RESOLUTION: A COMPARATIVE STUDY

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.