Research on Shaking Force with Ground-Roll Suppression Based on Fast Discrete Curvelet Transform

Video steganography mainly deals with secret data transmission in a carrier video file without being visually noticeable by intruders. Video steganography is preferred over image steganography because a video carries more space in comparison to an image. The main concept of information hiding consists of a cover media, which is a greyscale or a color video, a secret data, which is an image or text, and a stego key. Here a secure video steganography method has been proposed which uses Curvelet Transform for secret data embedding, Elliptic Curve Cryptography for stego key encryption and a threshold algorithm for the determination of the amount of secret data to be encoded per frame. A video is a collection of various frames. The frames are selected randomly from the cover video and the frame number of the respective frames has been indexed in the stego key to find the secret data embedding location. Here, the selection of frames in a sequential manner has been avoided to improve security. For enhanced security, the stego key is also encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES). Fast Discrete Curvelet Transform (FDCT) has been applied to the frames of the cover video and the curvelet coefficients have been modified to obscure the secret data to produce the stego video.

Image fusion based on the Fourier and wavelet transform methods retain rich multispectral details but less spatial details from source images. Wavelets perform well only at linear features but not at non linear discontinuities because they do not use the geometric properties of structures. Curvelet transforms overcome such difficulties in feature representation. In this paper, we define a novel fusion rule via high pass modulation using Local Magnitude Ratio (LMR) in Fast Discrete Curvelet Transforms (FDCT) domain. For experimental study of this method Indian Remote Sensing (IRS) Resourcesat-1 LISS IV satellite sensor image of spatial resolution of 5.8m is used as low resolution (LR) multispectral image and Cartosat-1 Panchromatic (Pan) of spatial resolution 2.5m is used as high resolution (HR) Pan image. This fusion rule generates HR multispectral image at 2.5m spatial resolution. This method is quantitatively compared with Wavelet, Principal component analysis (PCA), High pass filtering(HPF), Modified Intensity-Hue-Saturation (M.IHS) and Grams-Schmidth fusion methods. Proposed method spatially outperform the other methods and retains rich multispectral details.

The feature extraction plays a very important role in iris recognition. Recent researches on multiscale analysis provide good opportunity to extract more accurate information for iris recognition. In this work, a new directional iris texture features based on 2-D Fast Discrete Curvelet Transform (FDCT) is proposed. The proposed approach divides the normalized iris image into six sub-images and the curvelet transform is applied independently on each sub-image. The anisotropic feature vector for each sub-image is derived using the directional energies of the curvelet coefficients. These six feature vectors are combined to create the resultant feature vector. During recognition, the nearest neighbor classifier based on Euclidean distance has been used for authentication. The effectiveness of the proposed approach has been tested on two different databases namely UBIRIS and MMU1. Experimental results show the superiority of the proposed approach.

Digital watermarking technology means that the hidden information is embedded in digital multimedia data by way of signal processing. In this paper, we firstly analyze the Fast Discrete Curvelet Transforms (FDCT) and Discrete Wavelet Transforms (DWT) which are usually used in image processing and figure out that DWT can not approximate optimally the image which contains the line singularity. And then a new encrypted embedded algorithm is presented based on FDCT. Based on bit-plane of gray image, the algorithm makes use of the advantage of FDCT and the watermark is encrypted by Chaotic Map and Arnold Transforms (AT) which is imbedded into the proper coefficient of FDCT. The result of our experiment shows that this algorithm is robust and transparent compared with DWT.

This article presents an efficient method for power quality events (PQEs) detection and classification based on Curvelet transform (CT) and optimized extreme learning machine (OELM). Initially, various PQEs signal data are extracted even under noisy and simultaneously occurred multi-event conditions to reflect the real-time conditions. Relevant features of all these extracted signals are computed by using a multi-resolution and multidirectional fast discrete CT (FDCT) approach. These features are used to classify the events accurately by the proposed OELM. In this classification strategy, a modified differential evolution (MDE) is introduced to optimize the conventional ELM to enhance its precision rate. The proposed novel approach is tested and analyzed under various noisy conditions with 20 dB, 30 dB, and 50 dB with single and multi PQEs conditions. Comparative results with other recently proposed approaches by various authors reveal that the proposed CT- and OELM-based classifier titled CT-OELM can be considered as a competitive choice for implementing in the real-time monitoring system.

Batik is one of the cultural heritages in Indonesia. Batik has many types spread around Indonesia. Related to the diversity of batik, an effort to develop a database to preserve batik information is required. Searching batik information from the database by using keywords such as the province name where a batik came from, sometimes is difficult. In some cases, people only has a batik image without knowing any additional information, such as motif name and it's origin. Attaching a modul to classify batik image automatically into the database will be very useful, so that people can search more information about batik by inputting a batik image. This research proposes batik image classification using Fast Discrete Curvelet Transform (FDCT) and Hue Saturation Value (HSV) space as the representation of texture and color features, and K Nearest Neighbour (KNN) as the classifier. The experiment give a good result, which is showed by the worst classification error rate 3.33% for combined features vector.

Detecting the changes within a single image is significant for applications like planogram compliance where objects of same type are arranged in a sequence. This article discusses on detection of such objects which differ in color, texture and shape from other objects in the given image. In this proposed algorithm, the given image is converted to HSV color space and it is partitioned into regions; Fast Discrete Curvelet Transform (FDCT) coefficients are extracted. In order to identify changed regions in the given image, salient points are detected using the Features from Accelerated Segment Test (FAST) corner detection algorithm. It has been observed that the value channel content of the HSV image is much significant in extracting useful features for change detection. Experimental results show an efficacy of 87% in detecting changes in a single row of a shelf image having same items arranged in a linear fashion.Keywords(FAST) corner detectionHSV color spaceChange detectionFDCTImage processing

Noise removal is essential in medical imaging applications in order to enhance and recover anatomical details that may be hidden in the data. The issues of Poisson noise occurrence in medical imaging due to the arrival of photon nature of light produced at the time of capturing of image have always been a concern. This paper addresses a novel approach for the removal of Poisson noise embedded in the biomedical images based on multi-resolution transform-based denoising and fusion approach. In this technique, first, the images are denoised separately, by applying Discrete Wavelet Transform (DWT)/Stationary Wavelet Transform (SWT) and Fast Discrete Curvelet Transform (FDCT) integrated with Rudin-Osher-Fatemi (ROF) model. Next, the distinct features extracted from the denoised images are fused, resulting in an enhanced and denoised image. The proposed method was tested and compared with other denoising techniques on a set of biomedical images. Subjective and objective evaluations show better performance of this new approach compared to existing techniques.

Deconvolution is an essential step in seismic data processing. Sparse-spike deconvolution often is used to enhance the resolution of the seismic image by adding a model-driven regularization term. However, this method does not consider the features of the data, nor does it exactly describe the relationship between seismic data and the desired attribute (such as seismic reflectivity). We have developed a data and model dual-driven seismic deconvolution method based on error-constrained joint sparse representation (SR) using borehole measurement and surface seismic data. The combined features of the borehole reflectivity and the surface seismic data can be obtained through joint dictionary learning. With the help of the joint dictionary, the relationship between seismic waveforms and reflectivity is captured by the sparse coefficients. We construct the regularization term of deconvolution by alternately decomposing the error of the synthesized data via sparse reconstruction and the observed seismic data. Unlike model-driven methods, the constraint term of the new method can be established by the error-constrained SR. Based on this SR, the initial model of reflectivity is obtained to realize the sparse deconvolution of seismic data under the constraint of borehole data features. In general, this method is a data and model dual-driven deconvolution. Synthetic and field data tests demonstrate that this method can effectively improve the resolution and accuracy of deconvolution.

To remove background noise from seismic data recorded by spatially dense arrays, we have developed a space-based denoising procedure using the discrete curvelet transform. Based on a detailed statistical characterization of noise coefficients through the empirical cumulative distribution function method within a pre-event time window, signal and noise can be separated effectively by nonlinear thresholding. After synthetic test, we applied this method on data from an industry 3D seismic experiment recorded at an array deployed near Utica, Ohio. The denoising results show good waveform consistency with a significantly enhanced signal-to-noise ratio. Our curvelet approach allows a more computationally efficient spatial–temporal localization analysis of seismic data than conventional curvelet techniques by avoiding the assumption of stationary Gaussian-distributed noise and can be implemented as a complement of time-domain wavelet methods with fewer frequency losses after denoising. This new method provides a fast and convenient way to recover signals from noisy recordings with dense 2D arrays, leading to a considerable improvement in data quality compared with conventional Fourier, wavelet, and curvelet methods. The partitioned seismic signals and noise would yield advanced earth structure imaging, small-event detection, ambient noise tomography, and others.

While seismic data contains an abundance of useful information about the subsurface, it is also contaminated with disturbing recorded energy, coherent and random noise. A crucial step in seismic data processing, therefore, is denoising. In this work, we use a U-Net-based encoder-decoder network that uses ResNeXt blocks rather than traditional convolutions as a denoising tool. Furthermore, we add self-attention to the ResNext blocks in the deeper part of the neural network architecture. We compare the results with those obtained with the same network, but without attention, in order to investigate, whether self-attention improves machine-learning-based processing of seismic data. The supervised training with the implemented attention mechanism leads to improved denoising results than without attention. The deleted noise contains less primary energy and thus leads to a better conservation of the desired seismic signal.

Data interpolation is a very important step in seismic data processing. At present, there are many methods based on deep learning to solve the problem of seismic data interpolation, but few studies adopt the idea of multi-scale residuals combined with attention mechanism. In this paper, a seismic data interpolation algorithm based on multi-scale residual attention network is proposed. Multi-scale convolution is applied to residual structure for the first time, and the information of seismic data is extracted adaptively by cascade of multi-scale residual blocks. Then, the output of each residual block is used as hierarchical feature for global feature fusion. In the stage of residual mapping, the residual channel attention block is introduced to promote the flow of information features and make the channel attention mechanism focus on the high frequency and edge information of seismic data, thus enhancing the ability of network representation. A lot of numerical experiments show that the algorithm have short computation time and can achieve good reconstruction results.

This paper identifies a novel feature space to address the problem of human face recognition from still images. This is based on the PCA space of the features extracted by a new multiresolution analysis tool called Fast Discrete Curvelet Transform. Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform. Inspired by these attractive attributes of curvelets, we introduce the idea of decomposing images into its curvelet subbands and applying PCA (Principal Component Analysis) on the selected subbands in order to create a representative feature set. Experiments have been designed for both single and multiple training images per subject. A comparative study with wavelet-based and traditional PCA techniques is also presented. High accuracy rate achieved by the proposed method for two well-known databases indicates the potential of this curvelet based feature extraction method.

Vehicle type/make recognition based on images captured by surveillance cameras is a challenging task in intelligent transportation system and automatic surveillance. In this paper, we comparatively studied two feature extraction methods for image description, i.e. a new multiresolution analysis tool called Fast Discrete Curvelet Transform and the pyramid histogram of oriented gradients (PHOG). Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform, which is particularly appropriate for the description of images rich with edges. PHOG represents the local shape by a histogram of edge orientations computed for each image sub-region, quantized into a number of bins, thus has the ascendency in its description of more discriminating information. A composite feature description from PHOG and Curvelet can further increase the accuracy of classification by taking their complementary information. We also investigated the applicability of the Rotation Forest (RF) ensemble method for vehicle classification based on the combined features. The RF ensemble contains a set of base multilayer perceptrons which are trained using principal component analysis to rotate the original axes of combined features of vehicle images. The class label is assigned by the ensemble via majority voting. Experimental results using more than 600 images from 21 makes of cars/vans show the effectiveness of the proposed approach. The composite feature is better than any single feature in the classification accuracy and the ensemble model produces better performance compared to any of the individual neural network base classifier. With a moderate ensemble size of 20, the Rotation Forest ensembles offers a classification rate close to 96.5%, exhibiting promising potentials for real-life applications.

Ear is a relatively new biometric among others. Many methods have been used for ear recognition to improve the performance of ear recognition systems. In continuation of these efforts, we propose a new ear recognition method based on curvelet transform. Features of the ear are computed by applying Fast Discrete Curvelet Transform via the wrapping technique. Feature vector of each image is composed of an approximate curvelet coefficient and second coarsest level curvelet coefficients at eight different angles. k-NN (k-nearest neighbour) is utilized as a classifier. The proposed method is experimented on two ear databases from IIT Delhi. Results achieved using the proposed method on publicly available ear database are up to 97.77% which show encouraging performance.