Subsampled Contourlet Transform Research Articles

Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

Medical Image Fusion is the synthesizing technology for fusing multimodal medical information using mathematical procedures to generate better visual on the image content and high-quality image output. Medical image fusion represents an indispensible role in fixing major solutions for the complicated medical predicaments, while the recent research results have an enhanced affinity towards the preservation of medical image details, leaving color distortion and halo artifacts to remain unaddressed. This paper proposes a novel method of fusing Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) using a hybrid model of Non Sub-sampled Contourlet Transform (NSCT) and Joint Sparse Representation (JSR). This model gratifies the need for precise integration of medical images of different modalities, which is an essential requirement in the diagnosing process towards clinical activities and treating the patients accordingly. In the proposed model, the medical image is decomposed using NSCT which is an efficient shift variant decomposition transformation method. JSR is exercised to extricate the common features of the medical image for the fusion process. The performance analysis of the proposed system proves that the proposed image fusion technique for medical image fusion is more efficient, provides better results, and a high level of distinctness by integrating the advantages of complementary images. The comparative analysis proves that the proposed technique exhibits better-quality than the existing medical image fusion practices.

An Improved Multi Focus Image Fusion Method Using Non Sub-sampled Contourlet Transform and Guided Filter

An Improved Multi Focus Image Fusion Method Using Non Sub-sampled Contourlet Transform and Guided Filter

Image Fusion using Non Subsampled Contourlet Transform in Medical Field

Image fusion is a powerful method and developing field in the area of image processing. The image fusion is a type of methodology that combines the two or more images into single more informative image. Image fusion is the process of assimilation of numerous input images into a new single fused image with highly informative than the input image. There are various image fusion transform techniques are proposed. Out of that techniques a Non-subsampled Counterlet transform includes shift invariant property, highly directionality, reduced the cost and more efficient information as compared to previous techniques such as wavelet transform(WT), DWT, LWT, MWT, CWT, Curvelet transform, Contourlet transform. In NSCT, we decompose the images into low frequency and high frequency using sparse representation and absolute-maximum rule respectively. The DGSR algorithm is used for the better performance of SR-based approach. Finally, to reconstruct the image we use inverse NSCT and output is fused image.

Implementation Of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image de-noising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multi-resolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different de-nosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation.

Dynamic Histogram Equalization for contrast enhancement for digital images

Magnetic Resonance Imaging (MRI) is an efficient tool, produced by applying radio waves and magnetic fields which is being useful in the diagnosis of various diseases like cancer, epilepsy and stroke etc. The quality of the resulting image is needed to be enhanced because it is challenging for the specialists to investigate. Modified Histogram Equalization on Fuzzy based Improved Particle Swarm Optimization (FIPSO) is proposed for Dynamic Histogram Equalization which resolves this problem through image contrast enhancement. The details of an images are captured by smoothing and it uses Gaussian function to distribute pixel intensity to nearest pixel. It uses normal distribution and here blur is removed by applying Non subsampled Contourlet Transform. Then local maxima are calculated to extract dark and bright pixel values. The smoothed images are fuzzified with TSK (Takagi-Sugeno-Kang) model and it provides importance to all the local maxima intervals. An Improved particle swarm optimization (IPSO) algorithm is obtained by combining Galactic Swarm Optimization (GSO) with PSO which equalizes histogram of an image. FIPSO algorithm is used to the minimum contrast images of MRI brain images. Non-subsampled Contourlet transform (NSCT) based modified histogram equalization enhances image contrast. Here IPSO generates optimum values and these value are used to calculate cumulative distribution function in histogram equalization. The quality measures demonstrate that the current equalization technique attains highest performance against existing techniques in terms of brightness and contrast.

Digital Watermarking for Image Authentication using Spatial-Scale Domain based Techniques

This paper aims in presenting a thorough comparison of performance and usefulness of concept of spatial-scale domain based techniques in digital watermarking in order to sustain the ownership, security and true authentication. Spatial-scale based image watermarking techniques provides the information of 2-Dimensional (2-D) signal at different scales and levels, which is desirable for image watermarking. Further, these techniques emerged as a powerful and efficient tool to overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques. The spatial-scale based watermarking techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT) have been selected for watermarking process. Further, the comparison of performance of the selected watermarking techniques have been carried out in terms of different metrics, such as Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Tamper Assessment Factor (TAF) and Mean Structural Similarity Index (MSSIM). Analysis of result shows that multi-directional and shift-invariant NSCT technique outperforms the SWT and DWT based image watermarking techniques in terms of authentication, better capture quality, and tampering resistance, subjective and objective evaluation.

Cloud-Based Data Offloading for Multi-focus and Multi-views Image Fusion in Mobile Applications

The mobile phone is currently the most wid- ely used and most convenient photographic device. Like many optical imaging devices the camera of mobile ph- one are far from flawless, such as limited field of view and depth of focus in photos. Due to its limited computing power, it is not possible to use a mobile phone to fuse images. In addition, the multimedia communication need a good network connection and bandwidth. In this paper, we solve this problem by proposing an image fusion strategy and mobile data offloading method based on cloud computing platform. We design a cloud computing environment to fuse the images captured by the mobile phone where mobile terminal uploads and downloads images to the cloud through 5G signal. And a continuous time Markov model is applied to offload the mobile network data. We fuse the original images to all-in-focus image and 3D stereo image based on Non Subsampled Contourlet Transform and Detail-preserving & Content-aware Variational multiple images fusion method, respectively. The experimental results show that the proposed image fusion and data offloading method achieves satisfactory image quality and execution performance.

An optimal weighted averaging fusion strategy for thermal and visible images using dual tree discrete wavelet transform and self tunning particle swarm optimization

Image fusion plays a vital role in providing better visualization of image data. In this paper, we propose a new algorithm that optimally combines information from thermal images with a visual image of the same scene to create a single comprehensive fused image. In this work, an improved version of particle swarm optimization alogithm is proposed to optimally combine the thermal and visible images. The proposed algorithm is named self tunning particle swarm optimization (STPSO). Because of the importance of the fusion rule, a weighted averaging fusion rule is formulated that uses optimal weights resulting from STPSO for the fusion of both high frequency and low frequency coefficients obtained by applying Dual Tree Discrete Wavelet Transform (DT-DWT). The objective function in STPSO is formulated with the twin objectives of maximizing the Entropy and minimizing the Root Mean Square Error (RMSE), which differentiates our work from existing fusion techniques. The efficiency of our fusion algorithm is also evaluated by adding Gaussian white noise to the source images. The fusion results are compared with existing multi-resolution based fusion techniques, such as Laplacian Pyramid (LAP), Discrete Wavelet Transform (DWT) and Non Sub-Sampled Contourlet Transform (NSCT). The simulation results indicate that the proposed fusion framework results in better quality fused images when evaluated with subjective and objective metrics. Comparision of these results with those from PSO shows that our algorithm outperforms generic PSO.

Comparison of Fusion Techniques for Very High Resolution Data for Extraction of Urban Land-Cover

This paper aims in presenting a thorough comparison of performance of pan sharpening techniques, belonging to spatial, spectral, scale-space and spatial-frequency domain, for Very High Resolution Satellite Data. With the availability of new Very High Resolution sensors, especially, World View-2 sensor, which provides data at sub-meter level, the need for fusion of Panchromatic (PAN) and Multispectral (MS) images has to be further investigated. Pan-sharpening techniques namely, Hue-Saturation-Intensity, Brovey Transform, Principal Components Analysis, Discrete Wavelet Transform, Stationary Wavelet Transform, Non Sub-sampled contourlet Transform and Pseudo Wigner Distribution (PWD) fusion method have been selected for the fusion of PAN and MS images of World View-2 sensor. Further, the comparison of performance of each of the techniques have been carried out by using various evaluation indicators, such as, Root Mean Square Error, Peak Signal-to-Noise Ratio, Correlation Coefficient, Universal Image Quality Index. It is found that PWD based fusion technique gives good result with a good trade-off between the preservation of spectral information and enhancement of spatial resolution.

A Technique to Preserve Edge Information in Single Image Super Resolution

Goal of image super resolution is to enhance the size of an image without upsetting the inherited information. The quality of an enhanced image is conserved if the information around all kind of edges is preserved. The proposed novel approach potted the information around curvature edges and hard edges (abrupt transition in intensity) using Non Sub-Sampled Contourlet Transform (NSCT) based learning process. Furthermore the smoothness of smooth edges (gradual transition in intensity) is preserved by using soft edge smoothness prior as a regularizing parameter. The validity of the proposed approach is proven through simulation on several images.

Fusion of Panchromatic and Multispectral Images Using Non Subsampled Contourlet Transform and FFT Based Spectral Histogram

Fusion of Panchromatic and Multispectral Images Using Non Subsampled Contourlet Transform and FFT Based Spectral Histogram

Single Image Super-Resolution via Non Sub-sample Contourlet Transform based Learning and a Gabor Prior

Enhancing the quality of image is a continuous process in image processing related research activities. For some applications it becomes essential to have best quality of image such as in forensic department, where in order to retrieve maximum possible information, image has to be enlarged in terms of size, with higher resolution and other features associated with it. Such obtained high quality images have also a concern in satellite imaging, medical science, High Definition Television (HDTV), etc. In this paper a novel approach of getting high resolution image from a single low resolution image is discussed. The Non Sub-sampled Contourlet Transform (NSCT) based learning is used to learn the NSCT coefficients at the finer scale of the unknown high-resolution image from a dataset of high resolution images. The cost function consisting of a data fitting term and a Gabor prior term is optimized using an Iterative Back Projection (IBP). By making use of directional decomposition property of the NSCT and the Gabor filter bank with various orientations, the proposed method is capable to reconstruct an image with less edge artifacts. The validity of the proposed approach is proven through simulation on several images. RMS measures, PSNR measures and illustrations show the success of the proposed method.

A Novel Statistical Fusion Rule for Image Fusion and its Comparison in Non Subsampled Contourlet Transform Domain and Wavelet Domain

Image fusion produces a single fused image from a set of input images. A new method for image fusion is proposed based on Weighted Average Merging Method (WAMM) in the Non Subsampled Contourlet Transform (NSCT) domain. A performance analysis on various statistical fusion rules are also analysed both in NSCT and Wavelet domain. Analysis has been made on medical images, remote sensing images and multi focus images. Experimental results shows that the proposed method, WAMM obtained better results in NSCT domain than the wavelet domain as it preserves more edges and keeps the visual quality intact in the fused image.

Performance Analysis Of Multi Source Fused Medical Images Using Multiresolution Transforms

Image fusion combines information from multiple images of the same scene to get a composite image that is more suitable for human visual perception or further image-processing tasks. In this paper the multi source medical images like MRI (Magnetic Resonance Imaging), CT (computed tomography) & PET (positron emission tomography) are fused using different multi scale transforms. We compare various multi resolution transform algorithms, especially the latest developed methods, such as; Non Subsampled Contourlet Transform, Fast Discrete Curvelet, Contourlet, Discrete Wavelet transform and Hybrid Method (combination of DWT & Contourlet) for image fusion. The fusion operations are performed with all Multi resolution transforms. The fusion rules like local maxima and spatial frequency techniques are used for selection in the low frequency and high frequency subband coefficients, which can preserve more information and quality in the fused image. The fused output obtained after the inverse transform of fused sub band coefficients. The experimental results show that the effectiveness of fusion approaches in fusing multi source images.