3,205 research outputs found
Single Frame Image super Resolution using Learned Directionlets
In this paper, a new directionally adaptive, learning based, single image
super resolution method using multiple direction wavelet transform, called
Directionlets is presented. This method uses directionlets to effectively
capture directional features and to extract edge information along different
directions of a set of available high resolution images .This information is
used as the training set for super resolving a low resolution input image and
the Directionlet coefficients at finer scales of its high-resolution image are
learned locally from this training set and the inverse Directionlet transform
recovers the super-resolved high resolution image. The simulation results
showed that the proposed approach outperforms standard interpolation techniques
like Cubic spline interpolation as well as standard Wavelet-based learning,
both visually and in terms of the mean squared error (mse) values. This method
gives good result with aliased images also.Comment: 14 pages,6 figure
Video Resolution Enhancement using DWT, SWT and CLAHE
One of an image details which has been always an vital concern in various image and video-processing applications, such as video resolution enhancement, feature extraction, and satellite image resolution enhancement is resolution. In recent advances Video Resolution enhancement has been envisioned to help in numerous applications and has turned out to be a hot research area. This opens up several technical challenges and immense application possibilities. The paper describes the three main categories - Contrast limited adaptive histogram equalisation (CLAHE), Discrete Wavelet Transform(DWT), Stationary Wavelet Transform(SWT). DWT uses filter for building the multi-resolution. SWT is an extension of the Standard Discrete Wavelet Transform to enhance the general details of an image. This study presents a novel resolution enhancement methods with future research are
Improvement of BM3D Algorithm and Employment to Satellite and CFA Images Denoising
This paper proposes a new procedure in order to improve the performance of
block matching and 3-D filtering (BM3D) image denoising algorithm. It is
demonstrated that it is possible to achieve a better performance than that of
BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm
parameter values according to noise level, removes prefiltering, which is used
in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual
quality get improved, and BM3D complexities and processing time are reduced.
This improved BM3D algorithm is extended and used to denoise satellite and
color filter array (CFA) images. Output results show that the performance has
upgraded in comparison with current methods of denoising satellite and CFA
images. In this regard this algorithm is compared with Adaptive PCA algorithm,
that has led to superior performance for denoising CFA images, on the subject
of PSNR and visual quality. Also the processing time has decreased
significantly.Comment: 11 pages, 7 figur
Satellite Image Enhancement Using Dual Tree Complex Wavelet Transform
Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree Complex Wavelet Transform (DT-CWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DT-CWT in this proposed enhancement technique. Inverse DT-CWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques
Satellite image resolution enhancement using discrete wavelet transform and new edge-directed interpolation
An image resolution enhancement approach based on discrete wavelet transform (DWT) and new edge-directed interpolation (NEDI) for degraded satellite images by geometric distortion to correct the errors in image geometry and recover the edge details of directional high-frequency subbands is proposed. The observed image is decomposed into four frequency subbands through DWT, and then the three high-frequency subbands and the observed image are processed with NEDI. To better preserve the edges and remove potential noise in the estimated high-frequency subbands, an adaptive threshold is applied to process the estimated wavelet coefficients. Finally, the enhanced image is reconstructed by applying inverse DWT. Four criteria are introduced, aiming to better assess the overall performance of the proposed approach for different types of satellite images. A public satellite images data set is selected for the validation purpose. The visual and quantitative results show the superiority of the proposed approach over the conventional and state-of-the-art image resolution enhancement
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