Literature Review on Single Image Super Resolution (original) (raw)
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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.
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.
Single Image Super Resolution Algorithms: A Survey and Evaluation
— Image processing sub branch that specifically deals with the improvement, of images and videos, resolution without compromising the detail and visual effect but rather enhances the two, is known as Super Resolution. Multiple (multiple input images and one output image) or single (one input and one output) low resolution images are converted to high resolution. Single image super resolution algorithms are more practical since multiple images are not always available. The paper presents a survey of recent single image super resolution methods that are based on the use of external database to predict the values of missing pixels in high resolution image.
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.
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.
Single-image reconstruction using novel super-resolution technique for large-scaled images
Soft Computing
A fast and novel method for single-image reconstruction using the super-resolution (SR) technique has been proposed in this paper. The working principle of the proposed scheme has been divided into three components. A low-resolution image is divided into several homogeneous or non-homogeneous regions in the first component. This partition is based on the analysis of texture patterns within that region. Only the non-homogeneous regions undergo the sparse representation for SR image reconstruction in the second component. The obtained reconstructed region from the second component undergoes a statistical-based prediction model to generate its more enhanced version in the third component. The remaining homogeneous regions are bicubic interpolated and reflect the required high-resolution image. The proposed technique is applied to some Large-scale electrical, machine and civil architectural design images. The purpose of using these images is that these images are huge in size, and processing such large images for any application is time-consuming. The proposed SR technique results in a better reconstructed SR image from its lower version with low time complexity. The performance of the proposed system on the electrical, machine and civil architectural design images is compared with the state-of-the-art methods, and it is shown that the proposed scheme outperforms the other competing 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...
A Review on Super Resolution Technique
In this paper, we summary about the different research papers that applicable to our topic of Research which mentioned above. Image Super Resolution is a most important subject of research in the area of Image Processing. The "super resolution image" refers as technique to produce the high resolution of image from single or multiple low resolution images. The basically idea for Super-Resolution (SR) is that the fusion of a sequence of low-resolution (LR) which are noisy and blurred images that create a higher resolution (HR) image. For super resolution which is process of restoring and de noising of image. 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.
International Journal of Engineering Research and Technology (IJERT), 2012
https://www.ijert.org/single-image-super-resolution https://www.ijert.org/research/single-image-super-resolution-IJERTV1IS10412.pdf These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods. The SR image approaches reconstruct a single higher-resolution image from a set of given lower-resolution images For the reconstruction stage a SR reconstruction model composed of the L1 normdata fidelity and total variation (TV) regularization is defined, with its reconstruction object function being efficiently solved by the steepest descent method. Other SR methods can be easily incorporated in the proposed framework as well. Specifically, the SR computations for multi-view images computation in the temporal domain are discussed.
A Novel Approach for Single Image Super Resolution using Statistical Mathematical Model
2021
Images with high resolution are always a necessity in almost all image processing applications. .Super Resolution is a method in image processing to create High Resolution image from several or single low resolution image so that high spatial frequency information can be recovered. SR methods are applied on LR images in order to increase spatial resolution for a new image. The super resolution processing includes two main tasks: up-sampling of the image, removing degradations that arise during the image capture. In effect, the super-resolution process tries to generate the missing high frequency components. Applications may include HDTV, biological imaging etc. In this work we deal the problem of producing a HR image from a single low-resolution image using some statistical mathematical model. Performance of these algorithms was checked by using objective image quality criteria PSNR, MSSIM and compared with other existing methods