Image Resolution Enhancement using DWT and Spatial Domain Interpolation Technique (original) (raw)
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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 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 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.
IMAGE RESOLUTION ENHANCEMENT USING WAVELET DECOMPOSITION
Satellite images are being used in many fields of research. One of the major issues of these types of images is theirresolution.In this correspondence, I propose an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified using high frequency subbands obtained through SWT. Then all these subbands are combined to generate a new high resolution image using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques. KEYWORDS: Discrete wavelet transform (DWT), Sationary Wavelet transform (SWT), interpolation, satellite image resolution enhancement, wavelet zero padding (WZP).
IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition
IEEE Transactions on Image Processing, 2011
In this correspondence, the authors propose an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.
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 Based Various Interpolation Techniques for High Resolution Image Enhancement Processing
Satellite images are used in many fields of Earth Science Research and development. One of the main concepts of these types of images is their resolution. In this paper, we propose a hybrid satellite image resolution enhancement technique based on the image pixels values. The high-frequency content sub band's images are obtained by the implementing SWT and DWT of the input image. In this technique the input image is decomposed into different sub band's content images like LL, LH, HL and HH from this four different sub band's images combined with low-resolution input image have been interpolated, followed by combining all these images to generate a new high resolution-enhanced image by using IDWT. In this way to achieve a high resolution image, an intermediate stage for estimating the high-frequency subbands has been interpolated and proposed. This proposed technique has been tested on different low resolution satellite benchmark images. The image quantitative to be analysis the PSNR, MSE, RMSE and entropy show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.
ETRI Journal, 2010
In this paper, we propose a new super-resolution technique based on interpolation of the high-frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The proposed technique uses DWT to decompose an image into different subband images. Then the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new super-resolved image by using inverse DWT. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon. The quantitative peak signal-to-noise ratio (PSNR) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques. For Lena's image, the PSNR is 7.93 dB higher than the bicubic 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...