Content adaptive single image interpolation based Super Resolution of compressed images (original) (raw)

Up-Scaling of the Compressed Images using Adaptive Image Interpolation

International Journal of Innovative Technology and Exploring Engineering, 2019

In this paper, a method of image up scaling has been proposed. In many applications the display of electronic devices needs up scaling of images, but some time size of original image is small, so it may require synthetically increasing the size of the image. Interpolation is one of the solutions for that. In the presented method, a new up-scaled image has been achieved, which is upgraded based on the original image pixels present in small images. In the paper, its working is defined for the different directions of the image. The well-known image quality matrices have been considered to evaluate the performance of proposed method. The method has outperformed than the other standard methods and results have been shown in the paper.

Design and implementation of interpolation algorithms for image super resolution

2012

During the process of imagery, the factors including the motion between earth and the platform, atmosphere disturbance, out of focus, non-ideal sampling and so on, all can make the images blurred and degraded. Super resolution technology is the signal processing based method which can detect and remove the blur caused by the imaging system as well as recover spatial frequency information. Super resolution imaging processes one or more low resolution images acquired from the same scene to produce a single higher resolution image with more details. Recently, it has been one of the most active research areas to get high-resolution image from a lowresolution image, and for the communication purpose it is necessary to compress the information. In this paper, the various decimation algorithms are designed and implemented which can be used to compress the information which is very helpful for the communication purpose. At the receiver side, the decimated images are interpolated by using different interpolation algorithms to get back the original information. The performance analysis (PSNR and MSE) is calculated to know the error between the input and output images. The system is implemented using Matlab GUI.

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.

A robust combination interpolation method for video super-resolution

Science and Technology Development Journal, 2013

This paper presents an efficient method for video super-resolution (SR) based on two main ideals: Firstly, input video frames can be separated into two components, nontexturing image and texturing image. Then each component image is applied to a compatible interpolation method to improve the quality of high-resolution (HR) reconstructed frame. Secondly, based on the approach that border regions of image details are the most lossy information regions from the sampling process. Therefore, a task of compensation interpolation is essential to increase the quality of the reconstructed HR images. From these discussions, we proposed an efficient method for video SR by combining the spatial interpolation in different texturing regions and the sampling compensation interpolation to improve the quality of video super-resolution. Our results shown that, the quality of HR frames, reconstructed by the proposed method, is better than that of other methods, , and in recently. The significant contr...

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: 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.

Single Image Super Resolution using Interpolation and Discrete Wavelet Transform

International Journal of Trend in Scientific Research and Development

An interpolation-based method, such as bilinear, bicubic, or nearest neighbor interpolation, is regarded as a simple way to increase the spatial resolution for the LR image. It uses the interpolation kernel to predict the missing pixel values, which fails to approximate the underlying image structure and leads to some blurred edges. In this work a super resolution technique based on Sparse characteristics of wavelet transform. Hence, we proposed a wavelet based super-resolution technique, which will be of the category of interpolative methods, using sparse property of wavelets. It is based on sparse representation property of the wavelets. Simulation results prove that the proposed wavelet based interpolation method outperforms all other existing methods for single image super resolution. The proposed method has 7.7 dB improvement in PSNR compared with Adaptive sparse representation and self-learning ASR-SL [1] for test image Leaves, 12.92 dB improvement for test image Mountain Lion & 7.15 dB improvement for test image Hat compared with ASR-SL [1]. Similarly, 12% improvement in SSIM for test image Leaves compared with [1], 29% improvement in SSIM for test image Mountain Lion compared with [1] & 17% improvement in SSIM for test image Hat compared with [1].

Multi-kernel based adaptive interpolation for image super-resolution

Multimedia Tools and Applications, 2012

This paper proposes a cost-effective and edge-directed image super-resolution scheme. Image super-resolution (image magnification) is an enthusiastic research area and is desired in a variety of applications. The basic idea of the proposed scheme is based on the concept of multi-kernel approach. Various stencils have been defined on the basis of geometrical regularities. This set of stencils is associated with the set of kernels. The value of a re-sampling pixel is obtained by calculating the weighted average of the pixels in the selected kernel. The time complexity of the proposed scheme is as low as that of classical linear interpolation techniques, but the visual quality is more appealing because of the edgeorientation property. The experimental results and analysis show that proposed scheme provides a good combination of visual quality and time complexity.

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.

Super-Resolution using Combination of Wavelet Transform and Interpolation Based Method

2013

Super-resolution is a technique of producing a high-resolution (HR) image from one or more lowresolution (LR) images. Classical interpolation based magnification techniques like nearest-neighbor, bilinear and bicubic interpolation results in a larger image along with undesirable artifacts like blurring, aliasing and ringing effects. So the aim of super-resolution is to provide a larger image with good quality (quality means an image with less undesirable artifacts). Previous super-resolution techniques are based on using multiple images and learning based methods but the idea here is to use a single image in the super-resolution process. Here we have used the combination of wavelet transform and interpolation based technique to achieve the super-resolution using a single image. First the edges of the image are enhanced using wavelet transform and then the magnification is done using an interpolation based method. A comparison of this algorithm with other technique is also done to pr...