High Frequency Sub-band Coefficients Research Articles

Image Resolution Enhancement by Using Stationary and Discrete Wavelet Decomposition

This work proposed an image resolution enhancement technique which is based on the interpolation of the high frequency subbands obtained by DWT. The proposed technique uses DWT to decompose an image into different subbands, and then the high frequency subband images have been interpolated. The interpolated high frequency subband coefficients have been corrected by using the high frequency subbands achieved by SWT of the input image. An original image is interpolated with half of the interpolation factor used for interpolation the high frequency subbands. Afterwards all these images have been combined using IDWT to generate a super resolved imaged. The proposed technique has been tested on well-known benchmark images, where their PSNR, Mean Square Error and Entropy results show the superiority of proposed technique over the conventional and state-of-art image resolution enhancement techniques.

Multifocus image fusion scheme based on feature contrast in the lifting stationary wavelet domain

Abstract For fusion of multifocus images, a novel image fusion method based on multiscale products in lifting stationary wavelet (LSWT) domain is proposed in this article. In order to avoid the influence of noise and select the coefficients of the fused image properly, different subband coefficients employ different selection principles. For choosing the low frequency subband coefficients, a new modified energy of Laplacian (EOL) is proposed and used as the focus measure to select the coefficients from the clear parts of the low frequency subband images; when choosing the high frequency subband coefficients, a novel feature contrast measurement of the multiscale products is proposed, which is proved to be more suitable for fusion of multifocus images than the traditional contrast measurement, and used to select coefficients from the sharpness parts of the high frequency subbands. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT-traditional-contrast-(LSWT-Tra-Con)-based image fusion methods, even though the source images are corrupted by a Gaussian noise, in terms of both visual quality and objective evaluation.

Image Fusion Algorithm Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

Pulse Coupled Neural Networks(PCNN) have characteristics in accord with human vision properties,Nonsubsampled Contourlet Transform (NSCT) can overcome the lacking of Shift-invariance in Contourlet Transform. So NSCT was used associated with PCNN in image fusion algorithms to make full use of their characteristics. Original images were decomposed to get the coefficients of low frequency subbands and high frequency subbands. The coefficients of low and high frequency subbands were processed by a modified PCNN. Matching degree of original images and spatial frequency were defined and used respectively in fusion rules. Fusion image was obtained by NSCT inverse transformation. Experimental result shows this method is better than Wavelet, Contourlet and traditional PCNN methods; it has bigger mutual information, so the fusion image include more information about original images.

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.

Fusion of remote sensing images based on nonsubsampled contourlet transform and region segmentation

The purpose of remote sensing images fusion is to produce a fused image that contains more clear, accurate and comprehensive information than any single image. A novel fusion method is proposed in this paper based on nonsubsampled contourlet transform (NSCT) and region segmentation. Firstly, the multispectral image is transformed to intensity-hue-saturation (IHS) system. Secondly, the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT. Then the NSCT coefficients of high and low frequency subbands are fused by different rules, respectively. For the high frequency subbands, the fusion rules are also unalike in the smooth and edge regions. The two regions are segregated in the panchromatic image, and the segmentation is based on particle swarm optimization. Finally, the fusion image can be obtained by performing inverse NSCT and inverse IHS transform. The experimental results are evaluated by both subjective and objective criteria. It is shown that the proposed method can obtain superior results to others.

SAR image classification based on alpha-stable distribution

We propose a classification algorithm that utilizes the alpha-stable distribution to model the texture features of synthetic aperture radar (SAR) images. The SAR image is first decomposed by stationary wavelet transform (SWT). After that, the alpha-stable distribution is applied to model the high-frequency subband coefficients of the image at each decomposition scale. A regression-type method is then used to estimate the alpha-stable distribution parameters, which form a feature vector that fully describes the texture. Finally, a SAR image classification algorithm is derived by exploiting this feature vector based on the support vector machines (SVM) approach. Because different combinations of alpha-stable distribution parameters contribute to differences in classification precision, a multilevel SVM (MSVM) classification algorithm is also presented to address the issue. Experimental results indicate that the proposed SAR image classification algorithm is effective and the MSVM algorithm improves the classification performance. Moreover, our proposed algorithm has low computational cost as only a small number of the alpha-stable distribution parameters are processed.

A Novel DWT Based Multi-focus Image Fusion Method

This paper presents a novel discrete wavelet transform (DWT) based approach for multi-focus image fusion. The method is developed by setting different fusion rules for combining the coefficients of low frequency and high frequency sub-bands separately followed by a consistency verification process. In our method, the coefficients in the low frequency domain are selected based on a maximum sharpness focus measure scheme, while choosing the high frequency sub-bands coefficients, a maximum neighboring energy based fusion scheme is proposed. The performance assessment of the proposed method was conducted and compared with several conventional existing fusion methods. Experimental results can clearly demonstrate that the proposed method is an effective and feasible multi-focus image fusion algorithm.

Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain

Multifocus image fusion aims at overcoming imaging cameras's finite depth of field by combining information from multiple images with the same scene. For the fusion problem of the multifocus image of the same scene, a novel algorithm is proposed based on multiscale products of the lifting stationary wavelet transform (LSWT) and the improved pulse coupled neural network (PCNN), where the linking strength of each neuron can be chosen adaptively. In order to select the coefficients of the fused image properly with the source multifocus images in a noisy environment, the selection principles of the low frequency subband coefficients and bandpass subband coefficients are discussed, respectively. For choosing the low frequency subband coefficients, a new sum modified-Laplacian (NSML) of the low frequency subband, which can effectively represent the salient features and sharp boundaries of the image in the LSWT domain, is an input to motivate the PCNN neurons; when choosing the high frequency subband coefficients, a novel local neighborhood sum of Laplacian of multiscale products is developed and taken as one type of feature of high frequency to motivate the PCNN neurons. The coefficients in the LSWT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT–PCNN-based image fusion methods even though the source image is in a noisy environment in terms of both visual quality and objective evaluation.

Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain

This paper presents a new multi-source image fusion scheme based on lifting stationary wavelet transform (LSWT) and a novel dual-channel pulse-coupled neural network (PCNN). By using LSWT, we can calculate a flexible multiscale and shift-invariant representation of registered images. After decomposing the original images using LSWT, a new dual-channel pulse coupled neural network, which can overcome some shortcomings of original PCNN for image fusion and putout the fusion image directly, is proposed and used for the fusion of sub-band coefficients of LSWT. In this fusion scheme, a new sum-modified-laplacian(NSML) of the low frequency sub-band image, which represent the edge-feature of the low frequency sub-band image in SLWT domain, is presented and input to motivate the dual-channel PCNN. For the fusion of high frequency sub-band coefficients, a novel local neighborhood modified-laplacian (LNML) measurement is developed and used as external stimulus to motivate the dual-channel PCNN. This fusion scheme is verified on several sets of multi-source images, and the experiments show that the algorithms proposed in the paper can significantly improve image fusion performance, compared with the fusion algorithms such as traditional wavelet, LSWT, and LSWT–PCNN in terms of objective criteria and visual appearance.

Research of Blind Watermark Detection Algorithm Based on Generalized Gaussian Distribution

In digital management, multimedia content and data can easily be used in an illegal way-- being copied, modified and distributed again. Copyright protection, intellectual and material rights protection for authors, owners, buyers, distributors and the authenticity of content are crucial factors in solving an urgent and real problem. In such scenario, digital watermark techniques are emerging as a valid solution. Blind watermark detection is a modern digital watermark technology with outstanding feature. A novel algorithm of Blind Watermark Detection based on Generalized Gaussian Distribution is proposed in this paper. To start with, this paper carries on the statistical analysis to the high frequency sub-band coefficients of wavelet and contourlet transform, then knowing the high frequency sub-band coefficients of wavelet and contourlet transform can be characterized by Generalized Gaussian Distribution. So a blind watermark detection algorithm can be designed according to the method of maximum likelihood estimator. Experimental results demonstrate that the performance of watermark detector is good based on Generalized Gaussian Distribution. The scheme is robust against most attack, so it is very effective and practical.

Research on Wavelet Based Autofocus Evaluation in Micro-vision

This paper presents the construction of two kinds of focusing measure operators defined in wavelet domain. One mechanism is that the Discrete Wavelet Transform (DWT) coefficients in high frequency subbands of in-focused image are higher than those of defocused one. The other mechanism is that the autocorrelation of an in-focused image filtered through Continuous Wavelet Transform (CWT) gives a sharper profile than blurred one does. Wavelet base, scaling factor and form to get the sum of high frequency energy are the key factors in constructing the operator. Two new focus measure operators are defined through the autofocusing experiments on the micro-vision system of the workcell for micro-alignment. The performances of two operators can be quantificationally evaluated through the comparison with two spatial domain operators Brenner Function (BF) and Squared Gradient Function (SGF). The focus resolution of the optimized DWT-based operators is 14% higher than that of BF and its computational cost is 52% approximately lower than BF's. The focus resolution of the optimized CWT-based operators is 41% lower than that of SGF whereas its computational cost is approximately 36% lower than SGF's. It shows that the wavelet based autofocus measure functions can be practically used in micro-vision applications.