Satellite Image Resolution Enhancement (original) (raw)
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SATELLITE IMAGE RESOLUTION ENHANCEMENT USING DISCRETE WAVELET TRANSFORM (DWT)
Satellite images are being used in many fields of research. Satellite images are used in many applications such as geosciences studies, astronomy, and geographical information systems. One of the major issue of these types of images is their resolution. Because of the low frequency nature of these images, they appears as a blurred image. Thus the resolution of these satellite images is very low. In this paper a new satellite image resolution enhancement technique have been proposed using discrete wavelet transform (DWT) . Here, a new satellite image resolution enhancement technique have been proposed based on the interpolation of the high-frequency sub bands (HH bands ) obtained by discrete wavelet transform (DWT) and the input image. The proposed resolution enhancement technique uses DWT to decompose the whole input image into different sub bands. Then, the high-frequency sub bands of the image and the input low-resolution input image have been interpolated, followed by combining all these images to generate a new super resolved image by using Inverse DWT (IDWT). The quantitative peak signal-to-noise ratio(PSNR) and root mean square error (RMSE) and visual results show the superiority of the proposed technique over the conventional and state-of- art image resolution enhancement techniques.
IJERT-Improvement of Satellite Image Resolution Using Discrete Wavelet Transform
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/improvement-of-satellite-image-resolution-using-discrete-wavelet-transform https://www.ijert.org/research/improvement-of-satellite-image-resolution-using-discrete-wavelet-transform-IJERTV2IS100240.pdf Satellite images are being used in many fields of research. One of the major issues of these types of images is their resolution. In this paper, we propose a new satellite image resolution enhancement technique based on discrete wavelet transform(DWT). The proposed technique uses DWT to decompose the input image into different sub bands. Then, the LL sub-band and estimated high frequency sub-band images are combined to generate a new resolution-enhanced image by using inverse DWT. In order to achieve sharper image, an intermediate stage for estimating the high frequency subbands has been proposed. The proposed technique has been tested on satellite image. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the existing image resolution enhancement technique. Also faster and simpler implementation is proposed compared to one mentioned in [3] Index terms-Discrete wavelet transform (DWT), Satellite image resolution enhancement.
Image Resolution Enhancement in Satellite Based On Discrete Wavelet Transform
Abstract: Satellite images are being used in many fields of research. One of the major issues of these types of images is their resolution. In this project, I propose a new satellite image resolution enhancement technique based on the interpolation of the high-frequency sub bands obtained by discrete wavelet transform (DWT) and the input image. Resolution of an image has been always an important issue in many image- and video-processing applications, such as video resolution enhancement, feature extraction, and satellite image resolution enhancement. Interpolation in image processing is a method to increase the number of pixels in a digital image. Interpolation has been widely used in many image processing applications, such as facial reconstruction,. The DWT operation results in four decomposed sub band images referred to low- low (LL), low-high (LH), high-low (HL), and high-high (HH). The frequency components of those sub bands cover the full frequency spectrum of the original image. The proposed resolution enhancement technique uses DWT to decompose the input image into different sub bands. Then, the high-frequency sub band images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate stage for estimating the high frequency sub bands has been proposed. Index Terms: Discrete wavelet transform (DWT), interpola- tion, satellite image resolution enhancement, wavelet zero padding (WZP).
The Researchers uses Satellite images for their Research work in many fields. The Satellite images have issues with their resolution. So, the images which loses their high frequency contents. And they appeared as blurred image. This paper proposes a resolution enhancement technique called Multi Wavelet Transform (MWT). This technique obtains the interpolation of high frequency sub bands and low resolution input image. The MWT uses Discrete Wavelet Transform (DWT) to split the image into sub bands. Then high frequency sub band images are obtained and the input images have been interpolated. Finally the inverse MWT have been applied to generate the enhanced high resolution satellite image. This paper analyses the performance of various image resolution techniques for satellite images. The proposed technique produces less artifacts with less Mean Square Error and increased values of Peak Signal to Noise Ratio.
Wavelet Based Resolution Enhancement for Low Resolution Satellite Images
2014
Satellite images play a major role in the analysis of land cover, topographic analysis, geosciences etc. There has always existed a tradeoff between the image resolution and the image cost. In this paper, a modified discrete wavelet transform and interpolation based technique is proposed for enhancing the resolution of satellite images having low resolution in such a way that a highly resolved satellite image can be obtained without losing any image information. The advent of DWT has given a major impetus to many techniques based on achieving super resolution starting with a single low resolution image. In the proposed method, DWT is employed on the input satellite image to decompose it into sub-bands then the high frequency subbands and the input low resolution satellite image have been interpolated to obtain four interpolated images which are later combined after minor alterations to the interpolated input image using IDWT. The quantitative peak signal-to-noise ratio (PSNR) and classification results show that the resolution has been enhanced to a good scale without losing any information content present in the satellite image. The quality assessment parameters also illustrate the supremacy of the proposed technique over the conventional techniques.
Satellite images are one of the most important tools used by meteorologist to determine the weather conditions. It is also used to know the behavior of atmosphere. Particular region of the satellite image will helpful for meteorologist to determine weather conditions and atmosphere. In order to enhance the particular region of the satellite image, the conversion of low resolution to high resolution is needed. Also, images captured by the satellite will be transmitted at low resolution to minimize the transmission power. To improve the quality of the satellite image at receiver side is a challenging task in the field of Communication Engineering. The quality of satellite image will be improve using Discrete Wavelet Transform (DWT) based decomposition and bi-cubic interpolation method. Bi-cubic interpolation method will helpful for enhancing the edges of the objects in the satellite image. MATLAB software will be used for the simulation purpose. Comparison of different parameters like Mean Square Error (MSE), Root Mean Square Error (RMSE), and Peak Signal to Noise Ratio (PSNR) is also done for superiority of proposed method.
Robust Satellite Image Resolution Enhancement Based On Interpolation Of Stationary Wavelet Transform
2013
In this paper an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the difference 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 difference 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, low contrast satellite image by using inverse DWT (IDWT). The GHE is the contrast enhancement technique applied to high resolution, low contrast satellite image thus we finally obtain high resolution, high contrast satellite image. The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art i...
Image Enhancement For Satellite Image
In this work, image resolution enhancement technique which generates sharper high resolution image. The proposed technique uses DWT to decompose a low resolution image into different sub bands. Then the three high frequency sub band images have been interpolated using bicubic interpolation. The high frequency sub bands obtained by SWT of the input image are being incremented into the interpolated high frequency sub bands in order to correct the estimated coefficients. In parallel, the input image is also interpolated separately Discrete wavelet transform (DWT) is one of the recent wavelet transforms used in image processing. DWT decomposes an image into different sub band images, namely low-low (LL), low high (LH), high-low (HL), and high-high (HH). Another recent wavelet transform which has been used in several image processing applications is stationary wavelet transform (SWT). In short, SWT is similar to DWT but it does not use down-sampling, hence the sub bands will have the same size as the input 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.
Comparison of Satellite Image Enhancement Techniques in Wavelet Domain
2012
In this study, a comparison of various existing satellite image resolution enhancement techniques in wavelet domain is done. Each method is analysed quantitatively and visually. There are various wavelet domain based methods such as Wavelet Zero Padding, Dual Tree-Complex Wavelet Transform, Discrete Wavelet Transform, Cycle Spinning and Undecimated Wavelet Transform. On the basis of analysis, the most efficient method is proposed. The algorithms take the low resolution image as the input image and then wavelet transformation using daubechies (db3) is used to decompose the input image into different sub band images containing high and low frequency component. Then these subband images along with the input image are interpolated followed by combining all these images to generate a new resolution enhanced image by an inverse process.