Low Frequency Sub-band Coefficients Research Articles

Image Steganography Using Modified DWT Technique

The modern computing world revolves around the word “DATA”, but just what is so intriguing about it? In today’s world, data is the power and business start realizing it because data can predict customer trends potentially, get increased sales, and help the organization to achieve newer heights. The technology has become so advanced and our topmost priority is to secure data.Here in this project, the high frequency coefficients produced by the discrete wavelet transform contain hidden messages. To enhance the quality of the images, low frequency sub-band coefficients are kept intact. Before embedding, some elementary mathematical operations are performed on the secret messages. These operations prevent messages from being stolen or destroyed by unauthorized internet users, and they do so while also providing adequate security.

Dual Wavelet Attention Networks for Image Classification

Global average pooling (GAP) plays an important role in traditional channel attention. However, there is the disadvantage of insufficient information to use the result of GAP as the channel scalar. At the same time, the existing spatial attention models focus on the areas of interest using average pooling or convolutional networks, but there is a loss of feature information and neglect of the structural feature. In this paper, dual wavelet attention is proposed, which can effectively alleviate the aforementioned problems and enhance the representation ability of CNNs. Firstly, the equivalence between the sum of the low-frequency subband coefficients of 2D DWT (Haar) and GAP is proved. On this basis, the statistical characteristics of low-frequency and high-frequency subbands are effectively combined to obtain the channel scalars, which can better measure the importance of each channel. In addition, 2D DWT can effectively capture the approximate and detailed structural features. Thus, wavelet spatial attention is proposed, which can effectively focus on the key spatial structural features. Different from traditional spatial attention, it can better curve the structural and spatial attention for different channels. The experiments are verified on four natural image data sets and three remote sensing scene classification data sets, which shows the effectiveness and versatility of the proposed methods. The code of this paper will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yutinyang/DWAN</uri> .

Image Denoising Method Based on Harmonic Filtering and Non-Subsample Shear Wave Block Matching in Wavelength Domain

&lt;p&gt;Efficient noise filtering is the difficulty of image denoising on the premise of preserving the edge and internal texture information of the image. Therefore, this paper focused on adaptive Gaussian variational model and block matching image denoising method in the wave domain from the non-local point of view. At first, a 3D block match harmonic filtering model was establish in that wavelet domain, 3D transform was use to represent that real signals in the match block group in a sparse form, then the shrinkage threshold was used to achieve the purpose of pre-denoising, and the wavelet transform was then used to extract the high frequency part of the predictor image for filtering. In order to avoid edge blurring, Laplace-Gaussian algorithm was used to construct a new operator into the diffusion model, and the wavelet coefficients were reconstructed to obtain the final approximation of the original image. Secondly, a block matching denoising model based on shear wave was proposed. In order to avoid ill-conditioned problem, The multi-scale geometric analysis method using shearlet transform can improve the edge protection ability, and a block matching denoising model based on shearlet is proposed. The new model predicts the scaling threshold according to the statistical characteristics of the histogram, performs hard threshold filtering on the high-frequency sub-band to obtain the processed sub-band coefficients, and performs block matching 3D filtering on the decomposed low-frequency sub-band coefficients to obtain the processed coefficients. All processed subband coefficients are inversely transformed and reconstructed to obtain a denoised image. The analysis and simulation results showed that the two new models can effectively suppress noise and improve the clarity, and the block matching denoising based on shear wave had more advantages.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

A Method for Medical Microscopic Images' Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains.

An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope’s image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the proposed algorithm delivers better noise immunity performance than other methods reviewed by this study while maintaining high sensitivity.

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.

Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain.

Medical image fusion is a process that aims to merge the important information from images with different modalities of the same organ of the human body to create a more informative fused image. In recent years, deep learning (DL) methods have achieved significant breakthroughs in the field of image fusionbecause of their great efficiency. The DL methods in image fusion have become an active topic due to their high feature extraction and data representation ability. In this work, stacked sparse auto-encoder (SSAE), a general category of deep neural networks, is exploited inmedical image fusion. The SSAE is an efficient technique for unsupervised feature extraction. It has high capability of complex data representation. The proposed fusion method is carried as follows. Firstly, the source images are decomposed into low- and high-frequency coefficient sub-bands with the non-subsampled contourlet transform (NSCT). The NSCT is a flexible multi-scale decomposition technique, and it issuperior to traditional decomposition techniques in several aspects. After that, the SSAE is implemented for feature extraction to obtain a sparse and deep representation from high-frequency coefficients. Then, thespatial frequencies are computed for the obtained features to be used for high-frequency coefficient fusion. After that, a maximum-based fusion rule is applied to fuse the low-frequency sub-band coefficients. The final integrated image is acquired by applying theinverse NSCT. The proposed method has been applied and assessed on various groups of medical image modalities. Experimental results prove that the proposed method could effectively merge the multimodal medical images, while preserving the detail information, perfectly.

An Image Fusion Algorithm based on Nonsampled Contourlet Transform

In this paper, a multisensor image fusion method based on non-sampling Contourlet transform is proposed. The transform has translation invariance, is suitable for expressing images with rich detail information and direction information, and can avoid the ringing effect introduced by general methods to the fused images. The image is decomposed by the transform to obtain high and low frequency components. The high-frequency component uses PCNN as the fusion rule, and the low-frequency component uses the golden section method to search the optimal low-frequency fusion weight, so as to fuse the low-frequency subband coefficients of multi-sensor images adaptively. Comparative experimental results show that the proposed method can achieve better fusion effect.

Image Contrast Enhancement Method Based on Nonlinear Space and Space Constraints

Remote sensing, environmental monitoring, pattern recognition, and other fields all use this critical and indispensable basic technology. The article proposes an image contrast enhancement detection algorithm based on a linear model to address the problem that the current global contrast enhancement detection algorithm does not have high classification accuracy under the low-intensity JPEG compression quality factor. To decompose the original image, use the biorthogonal wavelet transform. The improved fuzzy set enhancement algorithm is used for the low-frequency subband coefficient. The results obtained after simulating this algorithm show that it is very effective in improving contrast, enhancing image details, and suppressing noise. It has the ability to greatly improve the image’s visual effect, as well as the advantages of parameter adaptation and high algorithm efficiency.

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.

Image Fusion Based Multimodal Biometric Recognition

Biometric Authentication is a security process that replays on the unique biological characteristics of an individual. Biometric Authentication system compare a biometric data capture to stored, confirmed authentic data in a database. It is simply the process of verifying the identity using the measurements or other unique characteristics of the body, then logging us in a service, device and so on. It is an effective way to prove identity because it can’t be replicated. Multi focus Image fusion is a process of fusing two or more images to obtain a new one. Used to reduce the problems like blocking, ringing artifacts occurs because of DCT. The low frequency sub-band coefficients are fused by selecting coefficient having maximum spatial frequency. The goal is classifying the images to classes of authorized and unauthorized using multi class SVM. The fingerprint image and iris image are fused together using SWT, the features are extracted from the fused image and labelled using GLCM algorithm. The testing image is then compared with trained samples and classified as authorized or unauthorized by using FFNN.

Image fuzzy enhancement algorithm based on contourlet transform domain

In order to solve the problems of low contrast, global darkness and noise amplification in some hyperspectral and remote-sensing images, a new image fuzzy enhancement method based on contourlet transform (CT) domain is proposed. The algorithm includes the following four steps. Firstly, the cyclic translation method is used to suppress the pseudo-Gibbs phenomenon caused by the lack of translation invariance of the CT. Secondly, a nonlinear gain function is designed to enhance and suppress the high-frequency coefficients adaptively. Meanwhile, the multi-scale Retinex with mixed gray function is used to process the low-frequency sub-band coefficients. Then, the inverse translation and linear averaging and the inverse CT are performed on the enhanced coefficients, and finally the improved fuzzy contrast is used to enhance the texture and edge of the image globally. The experimental results show that the proposed method can make the image texture details more prominent, and enhance the overall visual effect of the images. Furthermore, the absolute mean difference and PSNR of images are also greatly improved .

A new scheme for watermark extraction using combined noise-induced resonance and support vector machine with PCA based feature reduction

This manuscript presents a new scheme for binary watermark extraction using the combined application of noise-induced resonance (NIR) and support vector machine (SVM). The principal component analysis (PCA) is incorporated to minimize the dimension of the feature set obtained from the attacked watermarked image. The scheme utilizes lifting wavelet transform to decompose the original image into three levels, and blocks of low frequency sub-band coefficients are used for embedding purpose. Reference and signature information is embedded by quantizing the maximum and minimum coefficients of the corresponding block. Whereas, to extract the watermark, NIR-based tuning operation is performed. The transformed coefficients of the attacked watermarked image are tuned using iterative procedure of NIR in such a way that the transformed coefficients change their state from low signal-to-noise ratio (SNR) to maximum SNR or enhanced state. Finally, the tuned coefficients are fed into the machine i.e. SVM to classify as binary classes (0 or 1) which result in the corresponding watermark extraction. Experimental results of the proposed algorithm demonstrate noteworthy robustness against various signal processing attacks and remarkable improvements comparing with some of the recent techniques. Also, the scheme fulfills the requirements of image integrity in case of new strategic attack (i.e. print attack).

Dual Video Watermarking Algorithm Based on SIFT and HVS in the Contourlet Domain

We propose dual un-compressed video watermarking algorithms based on human visual system and scale invariant feature transform (SIFT) in the contourlet domain. First, every frame is decomposed by contourlet transform to obtain the low-frequency band and several high-frequency sub-bands in different directions. The energy value of every high-frequency sub-band is calculated. The low-frequency sub-band and the high-frequency sub-bands of the largest energy values are chosen to be embedded into watermarking signals. Second, the first watermarking algorithm is realized to embed watermarking signals by adjusting the size ratio between the coefficient histograms of low-frequency sub-band because the coefficient histogram of low-frequency sub-band possesses invariance to conventional geometric transform. Third, to the second watermarking algorithm, the human visual masking threshold based on contourlet domain is calculated as the maximum embedding tolerance, which is embedded in the largest energy value of the high-frequency sub-band as watermarking signals to guarantee the imperceptibility. Finally, when the watermarked video frame suffered from geometrically attacks, the SIFT and fine corrections are combined to effectively correct and restore the synchronization between geometrically attacked video frames and watermarking signals. The experimental results indicate that: 1) the visual masking based on video motion information in the contourlet domain can effectively improve the transparency and 2) combining the histogram mapping in low-frequency sub-band coefficients with SIFT and fine corrections can resist common geometric attacks and therefore improve the robustness of the watermarking algorithm.

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.

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.

Spectral preservation fusion for remote sensing images using focus measure operators based on fast discrete curvelet transform and hyperspherical color space

How to preserve the spectral information when enhancing the spatial details is a key issue of remote sensing image fusion. The component substitution (CS)-based fusion methods can effectively enhance the spatial details while suffering spectral distortion, and multiresolution analysis (MRA)-based methods have advantages in preserving spectral information but are not satisfactory in terms of spatial details. This paper proposes a hybrid method to integrate the advantages of CS- and MRA-based approaches. The intensity image is first obtained from an original multispectral (MS) image by hyperspherical color space (HCS) transform; then, the intensity image and original panchromatic (PAN) image are decomposed by fast discrete curvelet transform (FDCT). The focus measure operators are introduced to fused low-frequency, middle-high frequency, and fine scale subband coefficients in curvelet domain with specific fusion strategies. The final fusion image is achieved by inverse FDCT and inverse HCS transform. From the aspects of subjective and objective quality assessments, the experimental results on various types of remote sensing images including IKONOS, QuickBird, and WorldView-2 indicate that compared with existing well-known algorithms and commercial softwares, the proposed method shows obvious advantages in preserving spectral information and maintaining the spatial details.

Infrared and Visible Images Fusion with Multi-visual Cues

Image fusion is an important technique which combines the original information from multiple input images into a single composite image. The fused images will be more beneficial to human visual perception or further computer processing tasks than any individual input. Most of the traditional infrared and visible fusion approaches perform the fusion on the assumption that the original information is measured by local saliency features such as contrast or gradient. There is little consideration of the “interesting” or “useful” information in global. In this paper, an infrared and visible image fusion method is proposed by considering the final aim of image fusion, the human visual perception and further image processing tasks. The fusion is implemented under the non-subsampled contourlet transform based image fusion framework. The low frequency sub-band coefficients which represent the intensity of the scene are fused with the weight map which is constructed by considering both visual saliency uniqueness and task-oriented objectness, and refined by spatial consistency with guide filter. The new fusion strategy ensures that the objects being “interesting” or “useful” are preserved in the fused image. Sixteen pairs of infrared and visual images are used to test the validation of the proposed method. The experimental results show obvious improvement of the proposed method in terms of both objective and subjective quality measurements corresponding to other methods.

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.

A Medical Image Fusion Method Based on NSCT and PCNN with Neighborhood Stimulation

In order to better guide clinical diagnosis and treatment, this paper proposes medical image fusion method based on NSCT (nonsubsampled contourlet transform) and PCNN (pulse coupled neural network) with neighborhood stimulation. A fusion rule based on PCNN with neighborhood standard deviation stimulation is adopted in low frequency sub-band coefficient. Through the application of the PCNN simplified model, the PCNN model is stimulated by neighborhood standard deviation according to human visual system. The low frequency sub-band coefficient is determined by firing times. When choosing the bandpass directional sub-band coefficients, a fusion rule based on region gradient energy is adopted according to directional characteristics of NSCT. The experiment results show that the proposed method provides abundant details and edge information over conventional methods in achieving the better quality of fusion.

Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

In this paper, a novel microscopy mineral image enhancement method based on adaptive threshold in non-subsampled shearlet transform (NSST) domain is proposed. First, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands. Second, the gamma correction is applied to process the low-frequency sub-band coefficients, and the improved adaptive threshold is adopted to suppress the noise of the high-frequency sub-bands coefficients. Third, the processed coefficients are reconstructed with the inverse NSST. Finally, the unsharp filter is used to enhance the details of the reconstructed image. Experimental results on various microscopy mineral images demonstrated that the proposed approach has a better enhancement effect in terms of objective metric and subjective metric.