High Frequency Sub-band Coefficients Research Articles

SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection

Hyperspectral image (HSI) anomaly detection (HSI-AD) has become a hot issue in hyperspectral information processing as a method for detecting undesired targets without a priori information against unknown background and target information, which can be better adapted to the needs of practical applications. However, the demanding detection environment with no prior and small targets, as well as the large data and high redundancy of HSI itself, make the study of HSI-AD very challenging. First, we propose an HSI-AD method based on the nonsubsampled shearlet transform (NSST) domain spectral information divergence isolation double forest (SI2FM) in this paper. Further, the method excavates the intrinsic deep correlation properties between NSST subband coefficients of HSI in two ways to provide synergistic constraints and guidance on the prediction of abnormal target coefficients. On the one hand, with the “difference band” as a guide, the global isolation forest and local isolation forest models are constructed based on the spectral information divergence (SID) attribute values of the difference band and the low-frequency and high-frequency subbands, and the anomaly scores are determined by evaluating the path lengths of the isolation binary tree nodes in the forest model to obtain a progressively optimized anomaly detection map. On the other hand, based on the relationship of NSST high-frequency subband coefficients of spatial-spectral dimensions, the three-dimensional forest structure is constructed to realize the co-optimization of multiple anomaly detection maps obtained from the isolation forest. Finally, the guidance of the difference band suppresses the background noise and anomaly interference to a certain extent, enhancing the separability of target and background. The two-branch collaborative optimization based on the NSST subband coefficient correlation mining of HSI enables the prediction of anomaly sample coefficients to be gradually improved from multiple perspectives, which effectively improves the accuracy of anomaly detection. The effectiveness of the algorithm is verified by comparing real hyperspectral datasets captured in four different scenes with eleven typical anomaly detection algorithms currently available.

Modeling of fractional differential equation in cloud computing image fusion algorithm

Abstract In order to solve the problems of poor image quality, low definition and loss of image information in traditional algorithms, a modeling research of fractional differential equation in cloud computing image fusion algorithm is proposed. Firstly, the method of image denoising and enhancement under the framework of fractional calculus theory and the improved algorithm based on it are discussed. A series of difficult problems in the image processing method based on fractional calculus are discussed. Then, the large data video image in cloud computing environment is fused in scale space through fractional differential equation, and the fused image is decomposed by lifting fractional differential equation to obtain the low-frequency subband coefficients and high-frequency subband coefficients in different scale space. For the low-frequency subband coefficients and high-frequency subband coefficients, their respective fusion schemes are given to obtain the lifting static small transform coefficients of the fused image. The fusion effect of the proposed algorithm is tested from both subjective and objective aspects.The results show that the entropy value of this algorithm is 7.1450, slightly higher than sparse coding algorithm and random walk algorithm, which shows that the fusion processing by this algorithm will not lose the amount of information contained in the image. Therefore, the image denoising and enhancement algorithm of fractional differential equation proposed in this paper has good subjective fusion effect, good clarity and quality of the fused image, and will not lose the information contained in the image.

Machine vision based insulator image chip drop fault recognition method

Abstract The paper studies the method of insulator image chip drop fault recognition based on machine vision to improve the recognition effect of insulator chip drop fault.Zhang’s calibration method was used to calibrate the internal parameters of binocular vision camera to improve the results in image acquisition. Insulator images were captured by binocular vision camera after calibration. The color image is converted to digital image and the insulator target image is extracted from the saturation component of a digital image by the optimal entropy threshold segmentation method. The nonlinear function is used to correct the high-frequency subband coefficients in different scales and different directions in the insulator target image, and the coefficients are used to extract the high-frequency information of different scales and directions.. The improved one-dimensional Hough transform detection algorithm is used to identify the ellipse in the edge image, that is, the insulator. The decision conditions of fault identification are designed to complete the fault identification of insulator drop. Experiments show that this method can clearly collect insulator images under different weather conditions, effectively segment insulator target images and accurately extract insulator edge information. The method can accurately identify the insulator drop fault based on the ellipse detection results. When the contrast and brightness factors are different, the f1-score value of fault recognition is higher and the fault recognition effect is better.

An Image Steganography Algorithm Based on PSO and IWT for Underwater Acoustic Communication

To achieve fast and secure information steganography in underwater acoustic channel scenarios, this paper proposes a PSO (Particle Swarm Optimization) and IWT (integer wavelet transform) based image steganography algorithm for underwater acoustic communication. Firstly, the underwater image to be embedded is interleaved and packed to generate the secret information code-stream to improve the detection and recovery performance of transmission errors. Secondly, the cover image is divided into non-overlapping blocks and IWT is applied to each block, which can save PSO search space and improve the optimal solution performance. Thirdly, the embedding position matrix of the high frequency sub-band coefficients is chaotic scrambled, and PSO is used to select the optimal key to obtain the optimal matching position for the secret information to produce the best stego-image quality. Finally, transmission error detection and recovery measures are applied to the extracted secret information to improve the reconstruction quality in underwater acoustic channel scenarios. Experimental results show that the proposed algorithm outperforms existing approaches in terms of information embedding capacity and steganographic visual quality, meeting the requirements of safety and real-time performance of underwater communication. Moreover, it also has the ability of error detection and recovery for harsh underwater acoustic channel.

Perceptual Fusion of Electronic Chart and Marine Radar Image

Electronic charts and marine radars are indispensable equipment in ship navigation systems, and the fusion display of these two parts ensures that the vessel can display dangerous moving targets and various obstacles on the sea. To reduce the noise interference caused by external factors and hardware, a novel radar image denoising algorithm using the concept of Generative Adversarial Network (GAN) using Wasserstein distance is proposed. GAN focuses on transferring the image noise distribution between strong and weak noise, while the perceptual loss approach is to suppress the noise by comparing the perceptual characteristics of the output after denoising. Afterwards, an image registration method based on image transformation is proposed to eliminate the imaging difference between the radar image and chart image, in which the visual attribute transfer approach is used to transform images. Finally, the sparse theory is used to process the high frequency and low frequency subband coefficients of the detection image obtained by the fast Fourier transform in parallel to realizing the image fusion. The results show that the fused contour has a high consistency, fast training speed and short registration time.

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.

Composite material terahertz image fusion based on PCNN and RGEDIM under non-subsampled shearlet transform

In order to solve the problem that defects of different scales have different terahertz imaging characteristics in fiber reinforced composites, the fusion processing method of two terahertz images with complementary defect information was studied. To reduce the Gibbs phenomenon, Non-subsampled Shearlet Transform (NSST) with the property of shift- invariance was used to decompose source images and get their low-frequency subband and high frequency subband coefficients. Regional variance was used as connection strength factor of the Pulse Coupled Neural Network (PCNN) in the low frequency coefficient fusion, which is more according with human visual characteristics. In the fusion of high frequency coefficients, the Regional Gradient Energy of Direction Information Measure (RGEDIM) was introduced to extract the edge, texture and other details of the image and integrated them into the final image, the impact of noise on image fusion was reduced better. Finally, the fusion image was obtained through NSST inverse transform. The experimental results show that this method is superior to wavelet, Non-subsampled Contourlet Transform (NSCT) and traditional PCNN method, the fusion image has more mutual information and contains more original image information, all the defects of the source image can be clearly seen on the fusion image.

A Contrast Enhancement Method for Improving Surface Defects Detection of Elevator Compensation Chain

A contrast enhancement method based on the adaptive noise threshold estimation and logarithmic function in nonsubsampled contourlet transform (NSCT) domain is proposed, which can improve the defects segmentation accuracy of the elevator compensation chain. After extracting region of interest (ROI) according to the spatial location of defects, it is transformed into NSCT domain, where the high-frequency subband coefficients corresponding to noise is suppressed by the adaptive noise threshold estimation. Then, the logarithmic function transformation is used to enhance the image edges to a variable extent. Finally, the enhanced image is segmented by watershed algorithm based on laws texture analysis. Experimental results demonstrate that the performance of the proposed method is superior to the existing methods in terms of both the quality of contrast enhancement and the segmentation accuracy.

Multi-Focus Image Fusion Based on Residual Network in Non-Subsampled Shearlet Domain

In order to obtain a panoramic image which is clearer, and has more layers and texture features, we propose an innovative multi-focus image fusion algorithm by combining with non-subsampled shearlet transform (NSST) and residual network (ResNet). First, NSST decomposes a pair of input images to produce subband coefficients of different frequencies for subsequent feature processing. Then, ResNet is applied to fuse the low frequency subband coefficients, and improved gradient sum of Laplace energy (IGSML) perform high frequency feature information processing. Finally, the inverse NSST is performed on the fused coefficients of different frequencies to obtain the final fused image. In our method, we fully consider the low frequency global features and high frequency detail information in image by using NSST. For low-frequency coefficients fusion, we can also obtain the spatial information features of low-frequency coefficient images by using ResNet, which has a deep network structure. IGSML can use different directional gradients to process high-frequency subband coefficients of different levels and directions, which is more conducive to the fusion of the coefficients. The experiment results show that the proposed method has been improved in the structural features and edge texture in the fusion images.

An Efficient Max-Min Rule Selection for Lung Image Fusion using Wavelet Energy Based Fuzzy Logic

In this paper, the fusion of lung images using Positron Emission Tomography (PET) and Computed Tomography to identify the cancer part in (CT) images based on Discrete Wavelet Transform (DWT) is presented. The fused lung image is more informative than their individual counterpart and consists of the necessary information to diagnose diseases at the earliest. At first, the PET and CT images are decomposed by DWT, and it produces low and high-frequency subbands. Then Wavelet Energy based Fuzzy Logic (WEFL) is employed to select the MAX-MIN rule for the fusion of low and high-frequency subband coefficients of PET and CT image features to identify cancer parts in lungs. Finally, the fused lung image in low frequency and high-frequency components are combined to reconstruct the image. Results show that the performance of lung image fusion for the identification of the cancer part in CT and PET images.

Novel Registration and Fusion Algorithm for Multimodal Railway Images with Different Field of Views

Objects intruding high-speed railway clearance do great threat to running trains. In order to improve accuracy of railway intrusion detection, an automatic multimodal registration and fusion algorithm for infrared and visible images with different field of views is presented. The ratio of the nearest to next nearest distance, geometric, similar triangle, and RANSAC constraints are used to refine the matching SURF feature points successively. Correct matching points are accumulated with multiframe to overcome the insufficient matching points in single image pair. After being registered, an improved Contourlet transform fusion algorithm combined with total variation and local region energy is proposed. Inverse Contourlet transform to low frequency subband coefficient fused with total variation model and high frequency subband coefficients fused with local region energy is used to reconstruct the fused image. The comparison to other 4 popular fusion methods shows that our algorithm has the best comprehensive performance for multimodal railway image fusion.

An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement

Based on the advantages of a non-subsampled shearlet transform (NSST) in image processing and the characteristics of remote sensing imagery, NSST was applied to enhance blurred images. In the NSST transform domain, directional information measurement can highlight textural features of an image edge and reduce image noise. Therefore, NSST was applied to the detailed enhancement of high-frequency sub-band coefficients. Based on the characteristics of a low-frequency image, the retinex method was used to enhance low-frequency images. Then, an NSST inverse transformation was performed on the enhanced low- and high-frequency coefficients to obtain an enhanced image. Computer simulation experiments showed that when compared with a traditional image enhancement strategy, the method proposed in this paper can enrich the details of the image and enhance the visual effect of the image. Compared with other algorithms listed in this paper, the brightness, contrast, edge strength, and information entropy of the enhanced image by this method are improved. In addition, in the experiment of noisy images, various objective evaluation indices show that the method in this paper enhances the image with the least noise information, which further indicates that the method can suppress noise while improving the image quality, and has a certain level of effectiveness and practicability.

Ripplet domain fusion approach for CT and MR medical image information

Multimodal medical image fusion (MIF) plays an important role as an assistant for medical professionals by providing a better visualization of diagnostic information using different imaging modalities. The process of image fusion helps the radiologists in the precise diagnosis of several critical diseases and its treatment. In this paper, the proposed framework presents a fusion approach for multimodal medical images that utilize both the features extracted by the discrete ripplet transform (DRT) and pulse coupled neural network. The DRT having different features and a competent depiction of the image coefficients provides several directional high-frequency subband coefficients. The DRT decomposition can preserve more detailed information present in the reference images and further enhance the visualization of the fused images. Firstly, the DRT is applied to decompose the reference images into several low and high-frequency subimage coefficients that are fused by computing the novel sum modified Laplacian and novel modified spatial frequency motivated pulse coupled neural model. This model is used to preserve the redundant information also. Finally, fused images are reconstructed by applying the inverse DRT. The performance of the proposed fusion approach is validated by extensive simulation on the different CT-MR image datasets. Experimental results demonstrate that the proposed method provides the better fused images in terms of visual quality along with the quantitative measures as compared to several existing fusion approaches.

Infrared and visible image fusion method based on sparse features

Since the object information cannot be extracted efficiently by the traditional infrared and visible image fusion algorithms, an infrared and visible image fusion method based on the non-subsampled shearlet transform (NSST) and sparse structure features is proposed to retain the context information on visible image in this study. Firstly, we decompose the source images into low-frequency sub-band and high-frequency sub-band coefficients by the NSST. Then, benefit from the advantage of PCA on extracting principle information, the fusion rule in low-frequency sub-bands coefficients are merged by using the PCA-based approach. Afterwards, to retain the sparse structures from source images better, we propose a novel sparse feature extraction on high-frequency sub-band coefficients and fuse high-frequency components of source images. Finally, the inverse NSST is employed to obtain the fused image. The experimental results demonstrate that the proposed method preserves the background information on visible image and highlights the structural information on infrared image.

Infrared and visible image fusion based on NSCT and stacked sparse autoencoders

To integrate the infrared object into the fused image effectively, a novel infrared (IR) and visible (VI) image fusion method by using nonsubsampled contourlet transform (NSCT) and stacked sparse autoencoders (SSAE) is proposed. Firstly, the IR and VI images are decomposed into low-frequency subbands and high-frequency subbands by using NSCT. Secondly, SSAE is performed on the low frequency subband of IR image to calculate the object reliabilities (OR) of the low frequency subband coefficients. Subsequently, an adaptive multi-strategy fusion rule based on OR is designed for the fusion of low frequency subbands and a choose-max fusion rule with the absolute values of high frequency subband coefficients are employed for the fusion of high frequency subbands. Experimental results show the proposed method is superior to the conventional methods in highlighting the infrared objects as well as keeping the background information in VI image.

Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder

Stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. Motivated by the advantages mentioned above, a novel stacked sparse autoencoder and shift invariant shearlet transform-based image fusion method is proposed. First, the source images are decomposed into low- and high-frequency subbands by shift invariant shearlet transform; second, a two-layer stacked sparse autoencoder is adopted as a feature extraction method to get deep and sparse representation of high-frequency subbands; third, a stacked sparse autoencoder feature-based choose-max fusion rule is proposed to fuse the high-frequency subband coefficients; then, a weighted average fusion rule is adopted to merge the low-frequency subband coefficients; finally, the fused image is obtained by inverse shift invariant shearlet transform. Experimental results show the proposed method is superior to the conventional methods both in terms of subjective and objective evaluations.

Medical Image Fusion Based on Sparse Representation and PCNN in NSCT Domain.

The clinical assistant diagnosis has a high requirement for the visual effect of medical images. However, the low frequency subband coefficients obtained by the NSCT decomposition are not sparse, which is not conducive to maintaining the details of the source image. To solve these problems, a medical image fusion algorithm combined with sparse representation and pulse coupling neural network is proposed. First, the source image is decomposed into low and high frequency subband coefficients by NSCT transform. Secondly, the K singular value decomposition (K-SVD) method is used to train the low frequency subband coefficients to get the overcomplete dictionary D, and the orthogonal matching pursuit (OMP) algorithm is used to sparse the low frequency subband coefficients to complete the fusion of the low frequency subband sparse coefficients. Then, the pulse coupling neural network (PCNN) is excited by the spatial frequency of the high frequency subband coefficients, and the fusion coefficients of the high frequency subband coefficients are selected according to the number of ignition times. Finally, the fusion medical image is reconstructed by NSCT inverter. The experimental results and analysis show that the algorithm of gray and color image fusion is about 34% and 10% higher than the contrast algorithm in the edge information transfer factor QAB/F index, and the performance of the fusion result is better than the existing algorithm.

Pleiades Satellite Remote Sensing Image Fusion Algorithm Based on Shearlet Transform

According to the characteristics of multi-spectral and panchromatic remote sensing images, this paper proposes a new image fusion algorithm based on nonsubsampled shearlet transform. Firstly, low frequency sub-band and high frequency directional sub-band is obtained by IHS color space conversion, and nonsubsampled shearlet transform apply to original multi-spectral images. Then, the adaptive weighted fusion rules is used to construct the low frequency sub-band coefficients, and regional consistency check and region-based local variance fusion rule are used to construct the high frequency sub-band coefficients. Lastly, the inverse of nonsubsampled shearlet transform and the inverse IHS transform are applied to the sub-band coefficients and then obtained the fused image. The comparison of simulation results with traditional fusion methods indicates that the proposed method have better fusion performance both in spectral characteristics and spatial resolution.

A novel infrared and visible image fusion algorithm based on shift-invariant dual-tree complex shearlet transform and sparse representation

In this paper, a novel shift-invariant dual-tree complex shearlet transform (SIDCST) is constructed and applied to infrared and visible image fusion. Firstly, the mathematical morphology is used for the source images. Then, the images are decomposed by SIDCST to obtain the low frequency sub-band coefficients and high frequency sub-band coefficients. For the low frequency sub-band coefficients, a novel sparse representation (SR)-based fusion rule is presented. For the high frequency sub-band coefficients, a scheme based on the theory of adaptive dual-channel pulse coupled neural network (2APCNN) is presented, and the energy of edge is used for the external input of 2APCNN. Finally, the fused image is obtained by performing the inverse SIDCST. The experimental results show that the proposed approach can obtain state-of-the-art performance compared with conventional image fusion methods in terms of both objective evaluation criteria and visual quality.

Infrared and visible images fusion based on RPCA and NSCT

Current infrared and visible images fusion algorithms cannot efficiently extract the object information in the infrared image while retaining the background information in visible image. To address this issue, we propose a new infrared and visible image fusion algorithm by taking advantage of robust principal component analysis (RPCA) and non-subsampled Contourlet transform (NSCT). Firstly, RPCA decomposition is performed on the infrared and visible images respectively to obtain their corresponding sparse matrixes, which can well represent the sparse feature of images. Secondly, the infrared and visible images are decomposed into low frequency sub-band and high-frequency sub-band coefficients by using NSCT. Subsequently, the sparse matrixes are used to guide the fusion rule of low frequency sub-band coefficients and high frequency sub-band coefficients. Experimental results demonstrate that our fusion algorithm can highlight the infrared objects as well as retain the background information in visible image.