Low Frequency Sub-band Coefficients Research Articles

Technique for gray-scale visual light and infrared image fusion based on non-subsampled shearlet transform

A novel image fusion technique based on NSST (non-subsampled shearlet transform) is presented, aiming at resolving the fusion problem of spatially gray-scale visual light and infrared images. NSST, as a new member of MGA (multi-scale geometric analysis) tools, possesses not only flexible direction features and optimal shift-invariance, but much better fusion performance and lower computational costs compared with several current popular MGA tools such as NSCT (non-subsampled contourlet transform). We specifically propose new rules for the fusion of low and high frequency sub-band coefficients of source images in the second step of the NSST-based image fusion algorithm. First, the source images are decomposed into different scales and directions using NSST. Then, the model of region average energy (RAE) is proposed and adopted to fuse the low frequency sub-band coefficients of the gray-scale visual light and infrared images. Third, the model of local directional contrast (LDC) is given and utilized to fuse the corresponding high frequency sub-band coefficients. Finally, the final fused image is obtained by using inverse NSST to all fused sub-images. In order to verify the effectiveness of the proposed technique, several current popular ones are compared over three different publicly available image sets using four evaluation metrics, and the experimental results demonstrate that the proposed technique performs better in both subjective and objective qualities.

Image fusion based on shearlet transform and regional features

In this paper, a new image fusion method is proposed based on the shearlet transform. In this method, to make the best use of the regional characteristics of the low frequency coefficients, the low frequency sub-band coefficients from different images are fused according to a rule based on regional energy. As for the high frequency sub-band coefficients, they are fused according to a novel rule upon comprehensive consideration of multiple regional features, such as regional variance, regional average gradient, regional spatial frequency. Experimental results show that the proposed algorithm for image fusion outperforms traditional ones.

A semi-fragile watermarking scheme for authenticating audio signal based on dual-tree complex wavelet transform and discrete cosine transform

In this paper, a novel semi-fragile watermarking scheme for authenticating an audio signal based on dual-tree complex wavelet transform (DT-CWT) and discrete cosine transform (DCT) is proposed. Specifically, the watermark data are efficiently inserted into the coefficients of the low-frequency sub-band of DT-CWT taking advantages of both DCT and quantization index modulation (QIM). First, the original digital audio signal is segmented and then performed with DT-CWT. Second, based on the energy compression property, the low-frequency sub-band coefficients of the DT-CWT domain are performed with DCT, and the DC component is utilized to embed one distorted watermark bit by the QIM technique. Finally, inverse DCT and DT-CWT are orderly implemented on the watermarked coefficients of each audio segment to get a watermarked audio signal. Simulation results show that the hybrid embedding domain constructed by DT-CWT and DCT is effective, and the proposed watermarking scheme is not only inaudible, but also robust against content persistent non-malicious audio signal processing operations, such as MP3 compression, noise addition, re-sampling, re-quantization, etc. Furthermore, the proposed scheme can effectively authenticate the veracity and integrity of audio content and greatly expands the applicability of the audio watermarking scheme.

基于磁性微泡对比剂的超声和磁共振影像融合算法

With the help of magnetic microbubble contrast agent, a new fusion scheme between ultrasound image and magnetic resonance image is presented in this paper. Magnetic microbubble contrast agent is used in both ultrasound and MRI imaging process to enhance and preserve most important information of interest. By using nonsubsampled contourlet transform (NSCT), source images are decomposed. Then, image edge segmentation, combined with an self-adaptive algorithm of dynamic weighted non-negative matrix factorization(DWNMF) is utilized to complete the fusion of low-frequency subband coefficients, the fusion course of bandpass directional subband coefficients can be realized by use of the model of spatial frequency-motivated pulse coupled neural networks. Finally, the ultimate fused image can be obtained with inverse NSCT. The experiment based on the above-mentioned scheme demonstrates that ultrasound-MRI fusion may be a promising method for obtaining high quality fusion image.

An Effective Image Fusion Method Based on Nonsubsampled Contourlet Transform and Pulse Coupled Neural Network

In order to solve the problem of spectral distortion and the fuzzy texture in visible and infrared image fusion technology, a novel visible and infrared image fusion method based on the Nonsubsampled Contourlet Transform (NSCT) and Pulse Coupled Neural Networks (PCNN) is proposed in this paper. First, we gain three components of visible image, luminance I, chrominance H and saturation S, using the IHS transform. Then, we gain three coefficients, low frequency sub-band, passband sub-band and high frequency coefficient by decomposing the component I and infrared image with the help of the NSCT. Next, we use weighted-sum method to fuse the low frequency sub-band and PCNN method to fuse the other sub-band coefficient respectively. At last, we gain the fusion image by using the inverse IHS transform on the fusion component I gained by the inverse NSCT transform. Experiments show that our method have better fusion quality and can be more better to keep the visible spectral and detail information than some traditional methods such as, Laplace method, Wavelet method and Lifting Wavelet method.

Image fusion based on nonsubsampled contourlet transform for infrared and visible light image

In this paper, a new method based on nonsubsampled contourlet transform (NSCT) is proposed to fuse the infrared image and the visible light image, which will produce a new fused image by which the target can be identified more easily. Firstly, two original images were decomposed into low frequency subband coefficients and the bandpass direction subband coefficients by using NSCT. Secondly, the selection of the low frequency subband coefficient and the bandpass direction subband coefficient is discussed in detail. The low frequency subband coefficients are selected based on the regional visual characteristics. For the selection of bandpass direction subband coefficients, this paper proposes a minimum regional cross-gradient method, and the cross-gradient is gained by calculating the gradient between the pixel of bandpass subbands and the adjacent pixel in the fused image of the low-frequency components. Comparison experiments have been performed on different image sets, and experimental results demonstrate that the proposed method performs better in both subjective and objective qualities.

NSCT-Based Multi-Sensor Image Fusion Algorithm

Against the defects that the favoritism method and average method in the multi-sensor image fusion are apt to impair the image contrast, an image fusion algorithm based on NSCT is proposed. Firstly, this algorithm applied NSCT to the rectified multi-sensor images from the same scene, then different fusion strategies were adopted to fuse the low-frequency and high-frequency directional sub-band coefficients respectively: regional energy adaptive weighted method was used for low-frequency sub-band coefficient; the directional sub-band coefficient adopted a regional-energy-matching program that combined weighted average method and selection method. Finally, the fusion image was obtained by NSCT inverse transformation. Experiments were conducted to IR and visible light image and multi-focus image respectively. And the fusion image was evaluated objectively. The experimental results show that the fusion image obtained through this algorithm has better subjective visual effects and objective quantitative indicators. It is also superior to the traditional fusion method.

Speckle reduction of SAR image based on morphological Haar wavelet

The existing speckle reduction algorithms of Synthetic Aperture Radar(SAR) image can efficiently reduce the speckle effects but unfortunately smear edges and details.A new method,based on morphological Haar wavelet,was proposed.In this method,the SAR image was firstly decomposed by 2-D morphological Haar wavelet.Thus,the edges,details and textures were well preserved in low-frequency sub-band.The speckle noise was mainly distributed in high-frequency sub-bands.Then,the average filtering and median filtering were run on the corresponding high frequency sub-bands according to the noise features.Finally,2-D morphological Haar wavelet inverse transform was carried on to low-frequency sub-band coefficients and filtered high-frequency sub-bands coefficients to reconstruct SAR image accurately.The experimental results show that the proposed method can not only filter the speckle noise efficiently,but well preserve the image textures and details of SAR image.The proposed method is better than the traditional Lee filtering,Frost filtering,Kuan filtering and wavelet soft-threshold overall.

An improved wavelet image lossless compression algorithm

The wavelet transform divides image into different but interrelated multi-resolution and multi-level sub-bands, so quantization coding strategy of image wavelet coefficient has great flexibility. To address to energy aggregation and correlated features of image wavelet coefficient, Hilbert and singular value truncating were introduced to wavelet to propose an Improved Wavelet Lossless Compression Algorithm (IWLCA). It mainly performs classification rearrangement on low-frequency sub-band coefficient of wavelet image in accordance with Hilbert curve, but conducts singular value truncating transform on high-frequency coefficient. Then entropy encoding was combined to implement lossless image compression. Experimental results show that IWLCA has high encoding efficiency, which can also effectively reduce encoding bit rate of lossless image compression. Compared with mainstream lossless algorithms as JPEG-LS and JPEG 2000, the compression rate was significantly improved.

A Novel Image Fusion Method Based on FRFT-NSCT

Nonsubsampled Contourlet transform (NSCT) has properties such as multiscale, localization, multidirection, and shift invariance, but only limits the signal analysis to the time frequency domain. Fractional Fourier transform (FRFT) develops the signal analysis to fractional domain, has many super performances, but is unable to attribute the signal partial characteristic. A novel image fusion algorithm based on FRFT and NSCT is proposed and demonstrated in this paper. Firstly, take FRFT on the two source images to obtain fractional domain matrices. Secondly, the NSCT is performed on the aforementioned matrices to acquire multiscale and multidirection images. Thirdly, take fusion rule for low-frequency subband coefficients and directional bandpass subband coefficients to get the fused coefficients. Finally, the fused image is obtained by performing the inverse NSCT and inverse FRFT on the combined coefficients. Three modes images and three fusion rules are demonstrated in the proposed algorithm test. The simulation results show that the proposed fusion approach is better than the methods based on NSCT at the same parameters.

Multi-focus image fusion based on the non-subsampled contourlet transform

In this paper, a new image fusion algorithm based on non-subsampled contourlet transform (NSCT) is proposed for the fusion of multi-focus images. The selection of different subband coefficients obtained by the NSCT decomposition is critical to image fusion. So, in this paper, firstly, original images are decomposed into different frequency subband coefficients by NSCT. Secondly, the selection of the low-frequency subband coefficients and the bandpass directional subband coefficients is discussed in detail. For the selection of the low-frequency subband coefficients, the non-negative matrix factorization (NMF) method is adopted. For the selection of bandpass directional subband coefficients, a regional cross-gradient method that selects the coefficients according to the minimum of the regional cross-gradient is proposed. Finally, the fused image is obtained by performing the inverse NSCT on the combined coefficients. The experimental results show that the proposed fusion algorithm can achieve significant results in getting a new image where all parts are sharp.

Infrared Image And Visible Light Image Fusion Based On Nonsubsampled Contourlet Transform And The Gradient Of Uniformity

The nonsubsampled contourlet transform (NSCT) is a new multi-resolution transform, which can give an asymptotic representation of edges and contours in image by virtue of its characteristics of multi-direction, flexible multi-scale and shift-invariant simultaneity. For fusion between visible light image and infrared image, this paper proposed a new method based on nonsubsampled contourlet transform and the visual characteristics of region. Firstly, two original images were decomposed into different frequency sub-band coefficients by using NSCT, and then the selection of the low frequency subband coefficient and the bandpass direction subband coefficient was discussed respectively. For the choice of low frequency subband coefficient, this paper proposed a new method that chose coefficient based on the visual characteristics of region and gradient characteristics. For the choice of bandpass direction subband coefficient, this paper uses a direct method that chose the coefficient based on maximum regional uniformity obtained by the visual characteristics of region. Finally, the fused image was obtained by performing the inverse NSCT on the combined coefficients. The experimental results show that this fusion algorithm proposed in this paper achieved significant results to extract target in the infrared image and preserve edge of the target. At the same time, it also achieved good results to effectively preserve image details in the visible light image.

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.

A robust audio watermarking scheme based on lifting wavelet transform and singular value decomposition

In this paper, a new and robust audio watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD) is proposed. Specifically, the watermark data is efficiently inserted in the coefficients of the LWT low frequency subband taking advantage of both SVD and quantization index modulation (QIM). The use of QIM renders our scheme blind in nature. Furthermore, the synchronization code technique is also integrated into our hybrid LWT–SVD audio watermarking method. Experimental and analysis results demonstrate that the proposed LWT–SVD method is not only robust against both general signal processing attacks and desynchronization attacks but also achieve a very good tradeoff between robustness, imperceptibility and payload. Comparisons with the typical and related audio watermarking algorithms also show that our proposed method outperforms most of the selected algorithms.

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.

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 ALGORITHM BASED ON NONSUBSAMPLED CONTOURLET TRANSFORM AND ESTIMATION THEORY

Aimed at the fusion of infrared and visual images, and their application demands, a new image fusion method was proposed based on the nonsubsampled contourlet transform and estimation theory. Firstly, the nonsubsampled contourlet transform was employed to decompose the source images into the low frequency subband coefficient and bandpass directional subband coefficients. Then, for the bandpass directional subband coefficients, the detail coefficients were modeled by the Gaussian mixture distributions and the EM algorithm was used in conjunction with the model to develop an iterative fusion procedure to estimate the model parameters and to produce the fused coefficients; for the fusion of the approximate subband coefficients, the rule was employed based on the energy of the pixel neighboring region. Finally, the fused image was obtained by applying the inverse nonsubsampled contourlet transform. The experimental results showed that the fusion scheme is effective and the fused image is better than that of using the wavelet transform and the contourlet transform.

Embedded zerotree wavelets coding based on adaptive fuzzy clustering for image compression

In this paper, a new quantization approach based on an adaptive fuzzy c-means clustering for image compression is presented. The fuzzy cluster theory is applied to quantizing the wavelet coefficients of low-frequency subband after the image has been decomposed by wavelet transform. The method can automatically label the importance degree of coefficients of wavelets, and get new constraints on membership condition by weighted average method of the importance and 1 q k = θ k ( 1 ) · 1 + θ k ( 2 ) · λ k , θ k ( 1 ) + θ k ( 2 ) = 1 . Based on this condition, we cluster again. The proof of convergence of the algorithm is given. The experimental results show that exacter reconstructed values of wavelet coefficients can be obtained at low bit-rates, the subjective and objective quality of the reconstructed image is improved. This technique is shown to yield PSNR of reconstructed images improvement from 0.2 dB to 2.8 dB. This paper has brought about some new ideas in combining the fuzzy cluster algorithm with the embedded zerotree wavelets algorithm.

Subband filters optimized for lost coefficient reconstruction

Transmission of digital subband coded images over lossy packet networks presents a reconstruction problem at the decoder. This correspondence addresses the design of a family of quadrature mirror filters (QMFs) that minimize the mean-squared error of mean-reconstructed low-frequency subband coefficients. The resulting filters perform well as QMFs as well as provide the desired reconstruction properties in the event of loss.