A SURVEY ON SUPER RESOLUTION TECHNIQUES (original) (raw)

Comparision of different Image Resolution Enhancement techniques using wavelet transform

—We propose an Image resolution enhancement techniques based on interpolation of the high frequency subband images obtained from wavelet zero padding-cycle spinning, discrete, stationary and Dual tree Complex wavelet transform (DT-CWT). In this study, a comparison of three different image resolution enhancement techniques in wavelet domain is done. Each method is analyzed quantitatively and visually. Based on the analysis, the most efficient method is proposed. All the three algorithms uses low resolution image as the input image and then wavelet transform is applied to decompose the input image into different high and low frequency subbands. Then these subband images along with the input image are interpolated. Finally all these images are combined to generate a new resolution enhanced image by using inverse process. Keywords— wavelet zero padding, discrete wavelet transform, stationary wavelet transform, Dual tree complex wavelet transform, interpolation.

WAVELET ANALYSIS BASED IMAGE SUPER RESOLUTION Prof

2017

The increase in demand and performance of personal computing digital image processing is widely being used in many applications. Digital image process has advantage in term of cost, speed and flexibility. The objective is to extract information from the scene is being viewed. Image resolution describes the amount of information contained by images. Resolution has been frequently referred as an important aspect of an image. Images are being processed in order to obtain more enhanced resolution. One of the commonly used techniques for image resolution enhancement is Interpolation. In this work, an image resolution enhancement technique has been proposed which generates sharper high resolution image. The proposed technique uses DWT to decompose a low resolution image into different subbands. Then the three high frequency sub-band images have been interpolated using bi-cubic interpolation. The high frequency sub-bands obtained by SWT of the input image are being incremented into the int...

Image Resolution Enhancement by Wavelet Transform

Abstract— Wavelet transform based images resolution improvement is new technique in which all low and high frequency bands are considered for improvement using interpolation techniques like linear and bicubic which scales image. In previous work only high frequency bands are used for interpolation here differently low pass band is also considered because LL low –low frequency band mainly consist directional information feature .The interpolated frequency sub band coefficients have been corrected by using the frequency sub bands achieved by SWT of the input image. An original image is interpolated with half of the interpolation factor used for interpolation the high frequency sub bands. Afterwards all these images have been combined using IDWT to generate super resolved image. We are interpolating it to achieve high resolution using directional information features of low low band. We are using the input image for the interpolation of low frequency sub band image.

Image Resolution Enhancement using Multi-wavelet Transforms with Interpolation Technique

IOSR Journal of Electrical and Electronics Engineering, 2014

Images with high resolution give better results for image processing applications. Image resolution enhancement is the process of manipulating an image so that resultant image is more suitable for specific application such as medical, agricultural, satellite image processing. This paper is based on image resolution enhancement by combination of SWT & DWT .In DWT main loss is on high frequency components. The edge loss in high frequency components of DWT is minimized by adding it with high frequency components of SWT which is transition invariant transform. The interpolated high frequency sub-bands of DWT are added with high frequency sub bands of SWT. Then the added high frequency sub-bands (of SWT & DWT) as well as the input image are interpolated by factor of α/2to enlarge the input image by factor of α. Afterwards all these images have been combined using IDWT to generate a super resolved image. Algorithm considering Haar function is developed for DWT & inverse IDWT instead of using db9/7. This obtained image is compared with original high resolution image. This technique has been tested on standard bench mark images. The quantitative (Peak signal to noise ratio) and visual results are superior over conventional image resolution enhancement techniques.

Image resolution enhancement using multi-wavelet and cycle-spinning

Proceedings of 2012 UKACC International Conference on Control, 2012

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...

Redundant Wavelet Transform Based Image Super Resolution

2013

The process of Super Resolution (SR) aims at extracting a high resolution image from low resolution image. The proposed technique uses Redundant Wavelet Transform to enhance the resolution of an image using a single low resolution image. The proposed method decomposes the input image into different subbands. Then all subbands are interpolated. Combining all the interpolated subbands using Inverse Redundant Wavelet Transform provides the proposed super resolution image. The algorithm is tested with various wavelet types and their performance is compared. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon images. The proposed method gives higher quantitative peak signal-tonoise ratio (PSNR) and visual results in comparison to other conventional and state-of-art image Super resolution techniques. Index Term — Image Super Resolution, Interpolation, Discrete Wavelet Transform, Redundant Wavelet Transform.

IMAGE RESOLUTION ENHANCEMENT BY USING WAVELET TRANSFORM-2

NOW day's resolution of image is an important issue in almost all image and video processing applications like, feature extraction, video resolution enhancement, and satellite image resolution enhancement. Satellite images are used in various applications like geoscientific studies, astronomy, and geographical information systems.

Super Resolution Image Achievement Through Edge Extraction and Non Local Means Via Wavelet Domain

This letter addresses the subject of generating a super resolution image (SR) image from one low-resolution (LR) input image with in the wavelet domain. To attain a high resolution image, an intermediate stage for estimating the high-frequency (HF) sub bands has been planned. This stage includes edge preservation procedure and mutual interpolation between the input LR image and therefore the HF sub band pictures, as performed via the separate wavelet transform (DWT). On the other side the low resolution image is subjected to Non Local Mean(NLM) filter and the output along with the HL,HL and LH sub bands undergoes IDWT process to produce the high resolution image.The output obtained clearly indicates that our proposed method provided better results in both objective and subjective criteria. Introduction The images and video sequences that are obtained from multiple fields like radar, optical, medical and that are telecasted on high-definition television, in electron microscopy, etc., are obtained from electronic devices that use a variety of sensors. Thereby, a preprocessing technique that allows enhancement of image resolution should be used. This step can be done by estimating a high-resolution (HR) image x(m, n) from the low-resolution (LR) image y(m, n) data that were obtained through a linear operator V that forms a degraded version of the unknown HR image, which was additionally contaminated by an additive noise w(m,n),ie.,

Super-Resolution Using Edge Modification through Stationary Wavelet Transform

2014 18th International Conference on Information Visualisation, 2014

In this paper, a super-resolution technique is proposed that uses a combination of bicubic interpolation and wavelet transform. Bicubic interpolation produces a high resolution image but is prone to blurring artifact. So the blurring artifact is reduced in the wavelet domain. The input low-resolution is up-sampled using bicubic interpolation. The edges of the resultant highresolution image are enhanced using stationary wavelet transform (SWT). SWT is applied to the image to produce sub-bands of the image and then these sub-bands are modified by multiplying with a boost value. Then these sub-bands are combined using inverse stationary wavelet transform (ISWT) to produce the final highresolution image. The quantitative and qualitative analysis illustrate that the proposed technique is provides superior results as compared to other existing techniques.

A SURVEY ON SUPER RESOLUTION TECHNIQUES (original) (raw)

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