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 p...

Super-resolution is the task of obtaining a high-resolution image of a scene given low resolution image(s) of the scene. Applications of super-resolution include forensic, satellite, medical imaging, surveillance, displaying video on large screens, etc [1]. Obtaining high-resolution images directly via better hardware (better image sensors, larger chip size) is quite costly. Furthermore, most o...

A blind single-image super resolution method is proposed to enhance the spatial resolution of the image with defocus blur. Firstly, according to the low resolution imaging model, a framework of blind singleimage super resolution reconstruction is presented. Secondly, utilizing Wiener filtering algorithm, the errorparameter curve of the defocus blurred image was generated, through which the defo...

Multi-frame image super-resolution is a procedure which takes several noisy lowresolution images of the same scene, acquired under different conditions, and processes them together to synthesize one or more high-quality super-resolution images, with higher spatial frequency, and less noise and image blur than any of the original images. The inputs can take the form of medical images, surveillan...

Image super-resolution is the task of obtaining a high-resolution (HR) image of a scene given low-resolution (LR) image(s) of the scene. In this project, we have focused on the task of super-resolution given a single LR image, which is usually the case. There exist many techniques in literature addressing this task, and we have considered two techniques having the essence of [1] and [2]. In fir...

The extraction of a single high-quality image from a set of low-resolution images is an important problem which arises in fields such as remote sensing, surveillance, medical imaging and the extraction of still images from video. Typical approaches are based on the use of cross-correlation to register the images followed by the inversion of the transformation from the unknown high resolution im...

This paper presents some new techniques for high resolution (HR) image processing and compares between them. The paper focuses on two main topics, image interpolation and image superresolution. By image interpolation, we mean extracting an HR image from a single Degraded low resolution (LR) image. Polynomial based image interpolation is reviewed. Some new techniques for adaptive image interpola...

In this paper we propose a vision system that performs image Super Resolution (SR) with selectivity. Conventional SR techniques, either by multi-image fusion or example-based construction, have failed to capitalize on the intrinsic structural and semantic context in the image, and performed “blind” resolution recovery to the entire image area. By comparison, we advocate examplebased selective S...

Image super-resolution algorithm improves image resolution in software. Before image tracking, it needs to enhance image resolution to improve the image tracking accuracy of the bionic eye. The traditional super-resolution reconstruction method can’t satisfy the accuracy and real-time system, so this paper proposes super-resolution image reconstruction algorithm based on SURF (Speeded up Robust...