Survey of 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.

International journal of advanced engineering, management and science, 2024

Image super-resolution (SR) is a pivotal task in computer vision and image processing, aiming to enhance the resolution and quality of low-resolution images. This review article provides an in-depth analysis and comparison of various image super-resolution techniques, including traditional methods and deep learning-based approaches. We discuss the underlying principles, algorithms, advantages, and limitations of each technique, along with their applications across diverse domains. Additionally, we highlight recent advancements, challenges, and future research directions in the field of image superresolution.

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 Computer Applications, 2016

This paper gives the various applications of Super Resolution (SR) in today's time modern digital image processing. It has been seen that SR implementation results are always increasing the performance of digital image processing techniques. Modern digital communication includes many applications which generate the data in the form of images and videos. Along with the other techniques of processing of images and videos, Super resolution of images has also grown. In the world of powerful processors and advanced display systems, SR has shown its existence and an overview of various applications based on super resolution has been given in this paper.

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.

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.

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.

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.