Wavelet Decomposition Of Image Research Articles

Fine-Grained and Multiple Classification for Alzheimer's Disease With Wavelet Convolution Unit Network.

In this article, we propose a novel wavelet convolution unit for the image-oriented neural network to integrate wavelet analysis with a vanilla convolution operator to extract deep abstract features more efficiently. On one hand, in order to acquire non-local receptive fields and avoid information loss, we define a new convolution operation by composing a traditional convolution function and approximate and detailed representations after single-scale wavelet decomposition of source images. On the other hand, multi-scale wavelet decomposition is introduced to obtain more comprehensive multi-scale feature information. Then, we fuse all these cross-scale features to improve the problem of inaccurate localization of singular points. Given the novel wavelet convolution unit, we further design a network based on it for fine-grained Alzheimer's disease classifications (i.e., Alzheimer's disease, Normal controls, early mild cognitive impairment, late mild cognitive impairment). Up to now, only a few methods have studied one or several fine-grained classifications, and even fewer methods can achieve both fine-grained and multi-class classifications. We adopt the novel network and diffuse tensor images to achieve fine-grained classifications, which achieved state-of-the-art accuracy for all eight kinds of fine-grained classifications, up to 97.30%, 95.78%, 95.00%, 94.00%, 97.89%, 95.71%, 95.07%, 93.79%. In order to build a reference standard for Alzheimer's disease classifications, we actually implemented all twelve coarse-grained and fine-grained classifications. The results show that the proposed method achieves solidly high accuracy for them. Its classification ability greatly exceeds any kind of existing Alzheimer's disease classification method.

Segmentation and region quantification of bubbles in small bowel capsule endoscopy images using wavelet transform

ObjectiveA large number of captured frames by the wireless capsule endoscopy have been contaminated with different amounts of bubbles. Bubbles can degrade the visualization quality of the small intestine mucosa. The aim of this study is to develop an objective method for evaluating the amount of bubbles in WCE images. MethodsFrames with varying levels of bubble occlusion were selected from the Kvasir capsule endoscopy dataset. The round shape bubbles have an edge in their boundaries. Edges in the spatial domain correspond to high-frequency bands in the frequency domain. Two automated edge detection approaches have been developed in a rule-based manner and evaluated to assess the amount of bubbles. The first approach involved high pass filtering using fast Fourier transform (FFT), while the second approach has been based on wavelet image decomposition and reconstruction by omitting approximation coefficients subband. ResultsBoth Fourier and wavelet transforms obtained approximately the same dice similarity score (DSC), and precision metrics, which were equal to 0.87, and 0.91, respectively. Based on the specificity measure, the FFT outperformed the Hough and wavelet transforms. However, the wavelet transform obtained a higher dice similarity score (DSC) (0.93), accuracy (0.95), and sensitivity metric (0.97) and was the fastest, with an execution time of 0.01 s per frame, making it suitable for real-time applications. ConclusionThe proposed technique provides an easy-to-implement method for quality reporting or objective comparison tools of different bowel preparation paradigms in real-time applications due to its fast execution time. The obtained results from two different datasets proved that the presented method has good generalization.

Unsupervised radar signal recognition based on multi‐block – Multi‐view Low‐Rank Sparse Subspace Clustering

In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters. For untagged signals, a new unsupervised recognition method based on the time-frequency (TF) analysis and Multi-view Low-Rank Sparse Subspace Clustering (MLRSSC) is proposed. Specifically, the authors use image wavelet decomposition to create four views for TF images (TFIs), which introduces the concept of multi-view subspace clustering into radar signal recognition. Compared with other advanced clustering algorithms, the authors' algorithm pays more attention to combining the inherent properties of TFIs. Aiming at the unique ridgeline distribution phenomenon of TFI, a multi-block technology capable of robustly distributing TFIs subspaces is proposed. Then, Multi-block and MLRSSC are combined to propose the Multi-block Joint Multi-view Subspace Clustering (M-MLRSSC). The simulation shows that in the TFIs data set with a certain signal-to-noise ratio, M-MLRSSC has obtained superior results, which is better than a variety of advanced subspace clustering algorithms.

Estimation of winter-wheat above-ground biomass using the wavelet analysis of unmanned aerial vehicle-based digital images and hyperspectral crop canopy images

ABSTRACT The crop above-ground biomass (AGB) is critically important for monitoring crop growth, and its accurate estimation can be used by agricultural managers to improve farmland management and to predict crop grain yield. Many studies have shown that models for the estimation of AGB for multiple crop growth stages based on optical remote sensing spectral indices (SIs) often underestimate the crop AGB in later growth stages due to saturation problems. The purpose of this study was to estimate winter-wheat AGB using (i) high-frequency information obtained from the image wavelet decomposition (IWD) of unmanned aerial vehicle (UAV)-based digital images of winter-wheat canopy (i.e. high-frequency IWD information), and (ii) variables obtained from the continuous wavelet transform (CWT) of hyperspectral images of winter-wheat canopy (i.e. hyperspectral CWT variables). Digital and hyperspectral images were acquired using a digital camera and an imaging spectrometer, both mounted on a UAV. Unlike optical SIs, high-frequency IWD information and hyperspectral CWT variables may not be limited by saturation problems for high winter-wheat canopy cover. Our results indicate that high-frequency IWD information and hyperspectral CWT variables both increase (decrease) with increasing winter-wheat AGB. A multiple linear stepwise regression technique was used to analyse the performance of (1) SIs, (2) CWT, (3) IWD, (4) SIs + CWT, (5) SIs + IWD, (6) CWT + IWD, and (7) SIs + CWT + IWD, for estimating AGB. Our results indicate that: (i) the method based only on optical SIs may not support the estimation of winter-wheat AGB for multiple growth stages (SIs: coefficient of determination (R 2) = 0.62, mean absolute error (MAE) = 1.30 t ha–1, root-mean-square error (RMSE) = 1.63 t ha–1); (ii) the combined use of the IWD of digital images of winter-wheat canopy and the CWT of hyperspectral images of crop canopy can support the estimation of winter-wheat AGB for multiple growth stages (CWT + IWD: R 2 = 0.85, MAE = 0.79 t ha–1, RMSE = 1.01 t ha–1), including later growth stages; and (iii) the combined use of SIs and the IWD of digital images of winter-wheat canopy can improve the estimation accuracy of winter-wheat AGB (SIs + IWD: R 2 = 0.80, MAE = 0.93 t ha–1, RMSE = 1.22 t ha–1), which may indicate that imaging spectrometers and cheap digital cameras have distinct advantages and can both be used to obtain AGB estimates using different methods. This work provides a new perspective on the use of high-frequency IWD information, hyperspectral CWT variables, and their combination to estimate AGB for multiple crop growth stages.

Multi-focus Image Fusion Method based on Wavelet Transform

In this paper, the wavelet decomposition of multi-focus images is used to fuse the low-frequency components and high-frequency components obtained after decomposition. In order to make more information of the original image in the merged image, the method of maximizing the coefficient and the method of maximizing the variance of the region mean when the high frequency coefficient is selected, and combining the average and the selection in the selection of the low frequency coefficient. The simulation experiment results and the objective evaluation index values show that the modification method can make up for the defects of the two original images and obtain a relatively complete and clear image.

Model inversion of BCVL based on DWD of MISR image

This paper puts forward a novel algorithm for the inversion of biophysical components of vegetation leaves (BCVL) by means of Remote Sensing (RS) image based on multi-scale two-dimension Discrete Wavelet Decomposition (DWD). With the aid of the MISR (multi-angle imaging spectro-radiometer) image, the multi-scale BCVL inversion is implemented with the algorithm on each scale of wavelet decomposition based on DWD of MISR image. The retrieved results of the rough-scale wavelet decomposition are regarded as a priori knowledge of the fine-scale inversion, and then the retrieved results of the rough-scale are also considered as the initial values of the fine-scale inversion. In this way, wavelet decomposition of MISR image is gradually refined, and the retrieved result is likewise gradually refined until the final retrieved result of BCVL is achieved in the end. The algorithm provides a novel solution scheme for the model inversion of BCVL with the aid of DWD of MISR image. Furthermore, this article also discusses the arithmetic principle of the model inversion of BCVL by employing the radiative transfer model of vegetation leaves (RTMV). On this basis, this manuscript builds the inversion model of BCVL based on RTMV. The proposed algorithm is applied specially to the model inversion of BCVL by using MISR image in this experiment, and satisfactory results are finally achieved. Taking the model inversion of LAI (leaf area index) as an example, the retrieved result of a higher precision is achieved in the experiment. Where, correlation coefficient between measured LAI and retrieved LAI is R=0.84, and root-mean-square error is RMSE=0.26. Experiment demonstrates that the proposed algorithm is very effectual and reliable in the model inversion of BCVL. The proposed algorithm opens up a novel algorithmic pathway for the model inversion of BCVL by means of DWD of MISR image.

An Image Watermarking Algorithm Based on Discrete Hopfield Neural Network Encryption

With the rapid development of science and information technology, it is more and more important to design a robust image watermarking algorithm to protect the intellectual property and information security of images. First of all, this paper encrypts the watermark information to improve the security of the watermarking algorithm through discrete Hopfield neural network based on chaos. Then, based on the image wavelet decomposition, the watermark information is embedded into the low and middle frequency wavelet coefficients according to the minimum Visual error and pixel position distortion. Experiments show that the algorithm has good security, robustness and invisibility.

Detection of Rail Fastener Based on Wavelet Decomposition and PCA

A new method based on wavelet decomposition and principal component analysis (PCA) is proposed to solve the problem that the traditional method cannot effectively and quickly detect the rail fastening nut. Through the wavelet decomposition of the rail fastener image, the high frequency component of the image can be removed, and the noise can be reduced and the running time of the algorithm can be reduced. The principal component analysis method is used to reduce the dimension of the image, and the minimum distance classifier is used to detect the rail fastener. The experimental results show that the proposed algorithm can effectively detect the missing state of rail fastener, and the algorithm is robust to the occlusion of the noise image and rail fastener image.

Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval

The proliferation of large number of images has made it necessary to develop systems for indexing and organizing images for easy access. This has made Content-Based Image Retrieval (CBIR) an important area of research in Computer Vision. This paper proposes a combination of features in multiresolution analysis framework for image retrieval. In this work, the concept of multiresolution analysis has been exploited through the use of wavelet transform. This paper combines Local Binary Pattern (LBP) with Legendre Moments at multiple resolutions of wavelet decomposition of image. First, LBP codes of Discrete Wavelet Transform (DWT) coefficients of images are computed to extract texture feature from image. The Legendre Moments of these LBP codes are then computed to extract shape feature from texture feature for constructing feature vectors. These feature vectors are used to search and retrieve visually similar images from large database. The proposed method has been tested on five benchmark datasets, namely, Corel-1K, Olivia-2688, Corel-5K, Corel-10K, and GHIM-10K, and performance of the proposed method has been measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods in terms of precision and recall.

Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study.

This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR).

Bio‐inspired image enhancement derived from a ‘rank order coding’ model

In this study, the authors propose a new method to enhance image information, based on wavelet decomposition and original partial reconstruction of image. This reconstruction called ‘asynchronous reconstruction’ is not carried out in the same way as the usual sequential one. It is based on rank order coding. In fact, while sequential reconstruction is to sum all or a part of the responses obtained for each scale of ‘coarse to fine’ decomposition, asynchronous reconstruction tries to be closer to human brain which uses a limited number of frequency channels. Actually, after wavelet decomposition, responses are sorted from top down for each pixel of the image. Final asynchronous reconstruction for each pixel is obtained by adding a chosen number of wavelet responses, beginning by the maximum response. So, at a given level of reconstruction, the pixel values do not come from the same frequency channels. The interest of this method has been tested on a face verification task using the IV2 biometric database. Stopping criterion for reconstruction can be a constant number of wavelet responses to use, but an adaptive process has been also investigated. Three criteria are explored: standard deviation, entropy and lost edges ratio.

Progressive tool condition monitoring of end milling from machined surface images

Indirect tool condition monitoring in end milling is inevitable to produce high-quality finished products due to the complexity of end-milling process. Among the various indirect tool condition monitoring techniques, monitoring based on image processing by analyzing the surface images of final product is gaining high importance due to its non-tactile and flexible nature. The advances in computing facilities, texture analysis techniques and learning machines make these techniques feasible for progressive tool flank wear monitoring. In this article, captured end-milled surface images are analyzed using gray level co-occurrence matrix–based and discrete wavelet transform–based texture analyses to extract features which have a good correlation with progressive tool flank wear. Contrast and second diagonal moment are extracted from gray level co-occurrence matrix and root mean square and energy are extracted from discrete wavelet decomposition of end-milled surface images as features. Finally, these four features are utilized to build support vector machine–based regression models for predicting progressive tool flank wear with 94.8% average correlation between predicted and measured tool flank wear values.

A case study on medical image enhancement based on II curvelet.

A method is presented in this paper for medical image enhancement based on II curvelet. After the wavelet decomposition of medical image, we continue to break down the high-frequency sub-images, in order to get more detailed information. We have also designed the corresponding gain weight function for the edge enhancement of low-frequency sub-images, using II curvelet to extract the edge information, which has advantages over the normal curvelet transform. This method has overcome the disadvantages of the present medical image enhancement algorithm based on wavelet theory, for instance, (1) most of the present methods only decompose the low-frequency sub-images to get detailed information; (2) they can not well extract the tumors or other large areas of edge information of medical images. In simulation experiments, we enhanced the mammography X-ray images of breast provided by Heilongjiang Provincial Tumor Hospital, and compared our algorithm with several traditional image enhancement methods. The results shows that with our enhancement algorithm based on II curvelet, the textures and edges of the image can be reflected clearly, and the calcifications in the image are independent, at the same time, this method has the superiority when enhancing the images which have been added noise to.

A Robust Image Steganography using CDF Lifting Scheme and Huffman Encoding

In the modern era, concerns about safeguarding the information from attacks are on rise. Here, 2-d lifting wavelet decomposition of a grayscale cover image through Cohen, Daubechies and Feauveau (2,2) lifting wavelet is performed and three secret images are shuffled and embedded in detail coefficients of the transform using approximation coefficients as indicator. Here, a novel Approximate Coefficients Value Indicator methodology is used to shuffle and embed the binary images in detail coefficients. Lossy and lossless steganographic techniques, followed by a lossless compression scheme using Huffman encoding are proposed. In lossy steganographic technique, secret images are embedded in LSB values of high frequency bands of the transform. In lossless technique, high frequency band values are expanded and images are embedded as previous method. Attacks such as Histogram attacks, statistical attacks are performed to check the robustness of the proposed algorithm. MSE and PSNR values are calculated to demonstrate the imperceptibility of the proposed method.

Model Inversion of BBPV Based on DWD of MISR RS Image

This letter puts forward a novel algorithm for the inversion of biophysical and biochemical parameter of vegetation leaves (BBPV) by means of the multiscale two-dimension discrete wavelet decomposition (DWD) of remote sensing (RS) image. With the aid of a multiangle imaging spectroradiometer (MISR) RS image, the multiscale BBPV inversion is implemented with the algorithm on each scale of wavelet decomposition based on DWD of MISR RS image. The retrieved results of the rough-scale wavelet decomposition are regarded as a priori knowledge of the fine-scale inversion, and then, the retrieved results of the rough-scale wavelet decomposition are also regarded as the initial values of the fine-scale inversion. In this way, the wavelet decomposition of the MISR RS image is gradually refined, and the retrieved result is likewise gradually refined until optimal BBPV is achieved in the end. Furthermore, this letter also probes the algorithm principle of the model inversion of BBPV, by employing the radiative transfer model of vegetation leaves (RTMV). Taking the model inversion of leaf area index as an example, a satisfactory retrieval result of higher precision is finally obtained. The experiment demonstrates that the proposed algorithm is very effectual and reliable in the model inversion of BBPV. The proposed algorithm opens up a novel algorithmic pathway for the model inversion of BBPV by means of DWD of MISR RS image.

A Kernel Clustering Algorithm With Fuzzy Factor: Application to SAR Image Segmentation

The presence of multiplicative noise in synthetic aperture radar (SAR) images makes segmentation and classification difficult to handle. Although a fuzzy C-means (FCM) algorithm and its variants (e.g., the FCM_S, the fast generalized FCM, the fuzzy local information C-means, etc.) can achieve satisfactory segmentation results and are robust to Gaussian noise, uniform noise, and salt and pepper noise, they are not adaptable to SAR image speckle. This letter presents a kernel FCM algorithm with pixel intensity and location information for SAR image segmentation. We incorporate a weighted fuzzy factor into the objective function, which considers the spatial and intensity distances of all neighboring pixels simultaneously. In addition, the energy measures of SAR image wavelet decomposition are used to represent the texture information, and a kernel metric is adopted to measure the feature similarity. The weighted fuzzy factor and the kernel distance measure are both robust to speckle. Experimental results on synthetic and real SAR images demonstrate that the proposed algorithm is effective for SAR image segmentation.

Combined Application of Color Histogram and Wavelet Techniques for Content based Image Retrieval

this paper, we have proposed a novel method of content based image retrieval using combination of color histogram and wavelet techniques. We have used RGB color space to exploit the color features of image both for color histogram and wavelet decomposition of images. F-norm theory is used to extract features from wavelet transformed image. For histogram technique Euclidean distance is used and for wavelet technique F-norm theory based criteria is used to calculate similarity. We have shown that how individual techniques perform and have given approach to use both techniques in combination. Using recall rate it is shown that results have improved when both techniques are used together. We have also implemented the system in parallel and using speedup parameter it is shown that it has reduced the processing time making it practically useful in real world applications.

Sudy on Determination of Cadmium Content in Rice

Wavelet function using a variety of experimental spectral decomposition of the image area, the results show that the spectral fractal dimension db series db5 wavelet decomposition of the third layer is the most stable and reliable signal. And cadmium contamination levels in two different areas of Changchun area, for example, on the Hyperion image wavelet decomposition to obtain details of the signal spectrum fractal dimension map, identifying the extent of a wide range of rice cadmium pollution stress. This method is rapid, macro, accurate monitoring of cadmium contamination of rice provides a convenient way.

An Effective Image Coding Method using Lattice Vector Quantization in Wavelet Domain

As the digital information must be stored and recovered in an effective ways, it is necessary to compress data in image coding, wavelet transform and lattice vector quantization are adopted in this paper to improve the compression effective of image coding. We first propose the two dimensional spatial wavelet decomposition of image, and then we present lattice Vector Quantization coding scheme for each image subband, lattice vector quantization can make full use of the correlation between the image wavelet coefficients to remove the information redundancy, and the coding method of lattice quantization can be effective because of applying the symmetries of the lattice. The establishment of rate distortion (RD) model suitable for lattice vector quantization of wavelet image coder is also important for image compression, study shows that the RD performance for the spatial subbands are fitted by an exponential form theoretical model, this yields an analytical solution to the bit rate distribution issue, we explore an effective rate control scheme by using the lagrangian optimization method to distribute the bit rate for the spatial subbands. The experimental results show that the proposed algorithm can achieve better compressing effect with minimum loss.

Visual Perception Based Objective Stereo Image Quality Assessment for 3D Video Communication

Stereo image quality assessment is a crucial and challenging issue in 3D video communication. One of major difficulties is how to weigh binocular masking effect. In order to establish the assessment mode more in line with the human visual system, Watson model is adopted, which defines visibility threshold under no distortion composed of contrast sensitivity, masking effect and error in this study. As a result, we propose an Objective Stereo Image Quality Assessment method (OSIQA), organically combining a new Left-Right view Image Quality Assessment (LR-IQA) metric and Depth Perception Image Quality Assessment (DP-IQA) metric. The new LR-IQA metric is first given to calculate the changes of perception coefficients in each sub-band utilizing Watson model and human visual system after wavelet decomposition of left and right images in stereo image pair, respectively. Then, a concept of absolute difference map is defined to describe abstract differential value between the left and right view images and the DP-IQA metric is presented to measure structure distortion of the original and distorted abstract difference maps through luminance function, error sensitivity and contrast function. Finally, an OSIQA metric is generated by using multiplicative fitting of the LR-IQA and DP-IQA metrics based on weighting. Experimental results shows that the proposed method are highly correlated with human visual judgments (Mean Opinion Score) and the correlation coefficient and monotony are more than 0.92 under five types of distortions such as Gaussian blur, Gaussian noise, JP2K compression, JPEG compression and H.264 compression.