Low Frequency Sub-band Research Articles

Blind and safety-enhanced dual watermarking algorithm with chaotic system encryption based on RHFM and DWT-DCT

This study proposes a novel blind watermarking algorithm based on radial harmonic Fourier moments (RHFMs) as well as discrete wavelet transform and discrete cosine transform (DWT-DCT) to achieve copyright protection. A non-adjacent coupled map lattice chaotic system was used to improve the safety in the watermark embedding process. First, a zero-watermark was obtained by performing DCT on the original image. Meanwhile, the direction information of the origin was calculated and embedded in the RHFM domain. During normal watermark embedding, the image-embedded direction information was transformed by a one-level DWT and the low-frequency sub-band was divided into 8×8 non-overlapping blocks. Each block was transformed by DCT, and two low-frequency coefficients were selected to embed the encrypted watermark. Experimental results demonstrated that the proposed algorithm is robust against rotation and other attacks. The analysis also showed that the proposed algorithm satisfies the invisibility of watermarking algorithms and has good security performance.

A Novel HDR Image Zero-Watermarking Based on Shift-Invariant Shearlet Transform

In this paper, a novel high dynamic range (HDR) image zero-watermarking algorithm against the tone mapping attack is proposed. In order to extract stable and invariant features for robust zero-watermarking, the shift-invariant shearlet transform (SIST) is used to transform the HDR image. Firstly, the HDR image is converted to CIELAB color space, and the L component is selected to perform SIST for obtaining the low-frequency subband containing the robust structure information of the image. Secondly, the low-frequency subband is divided into nonoverlapping blocks, which are transformed by using discrete cosine transform (DCT) and singular value decomposition (SVD) to obtain the maximum singular values for constructing a binary feature image. To increase the watermarking security, a hybrid chaotic mapping (HCM) is employed to get the scrambled watermark. Finally, an exclusive-or operation is performed between the binary feature image and the scrambled watermark to compute robust zero-watermark. Experimental results show that the proposed algorithm has a good capability of resisting tone mapping and other image processing attacks.

Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. Firstly, the dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. Then, the discrete wavelet transform (DWT) is utilized to obtain the low frequency sub-band (LFS) and high frequency sub-band (HFS). Next, the wavelet shrinkage threshold is applied to calculate the wavelet threshold corresponding to the HFS at different scales. Meanwhile, a new Garrate threshold function that introduces adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients, and the Gamma function is employed to correct the LFS coefficients. Finally, the PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can not only significantly improve the overall brightness and contrast of the degraded image but also suppresses the noise of dust and spray and enhances the image details. Compared with the similar algorithms of STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the indicator values of comprehensive performance of the proposed algorithm are increased by 205%, 195%, 200%, 185%, 185%, 85%, 140%, and 215%, respectively. Moreover, compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments.

OPTIMIZED DUAL-TREE COMPLEX WAVELET TRANSFORM AND FUZZY ENTROPY FOR MULTI-MODAL MEDICAL IMAGE FUSION: A HYBRID META-HEURISTIC CONCEPT

In recent times, multi-modal medical image fusion has emerged as an important medical application tool. An important goal is to fuse the multi-modal medical images from diverse imaging modalities into a single fused image. The physicians broadly utilize this for precise identification and treatment of diseases. This medical image fusion approach will help the physician perform the combined diagnosis, interventional treatment, pre-operative planning, and intra-operative guidance in various medical applications by developing the corresponding information from clinical images through different modalities. In this paper, a novel multi-modal medical image fusion method is adopted using the intelligent method. Initially, the images from two different modalities are applied with optimized Dual-Tree Complex Wavelet Transform (DT-CWT) for splitting the images into high-frequency subbands and low-frequency subbands. As an improvement to the conventional DT-CWT, the filter coefficients are optimized by the hybrid meta-heuristic algorithm named as Hybrid Beetle and Salp Swarm Optimization (HBSSO) by merging the Salp Swarm Algorithm (SSA), and Beetle Swarm Optimization (BSO). Moreover, the fusion of the source images’ high-frequency subbands was done by the optimized type-2 Fuzzy Entropy. The upper and lower membership limits are optimized by the same hybrid HBSSO. The optimized type-2 fuzzy Entropy automatically selects high-frequency coefficients. Also, the fusion of the low-frequency sub-images is performed by the Averaging approach. Further, the inverse optimized DT-CWT on the fused image sets helps to obtain the final fused medical image. The main objective of the optimized DT-CWT and optimized type-2 fuzzy Entropy is to maximize the SSIM. The experimental results confirm that the developed approach outperforms the existing fusion algorithms in diverse performance measures.

Data hiding in encryption–compression domain

This paper introduces a robust and secure data hiding scheme to transmit grayscale image in encryption-then-compression domain. First, host image is transformed using lifting wavelet transform, Hessenberg decomposition and redundant singular value decomposition. Then, we use appropriate scaling factor to invisibly embed the singular value of watermark data into the lower frequency sub-band of the host image. We also use suitable encryption-then-compression scheme to improve the security of the image. Additionally, de-noising convolutional neural network is performed at extracted mark data to enhance the robustness of the scheme. Experimental results verify the effectiveness of our scheme, including embedding capacity, robustness, invisibility, and security. Further, it is established that our scheme has a better ability to recover concealed mark than conventional ones at low cost.

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion.

The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.

Multifocus image fusion method using discrete fractional wavelet transform and improved fusion rules

In order to solve the problem of image blur caused by different focal points of multifocus images, this paper provides a noval multi-focus image fusion method which uses discrete fractional wavelet transform (DFRWT) and improved fusion rules. First, the DFRWT is utilized to decompose the source multifocus images at multiple scales. Then considering the different characteristics of low-frequency subband as well as high-frequency subband, the local region gradient information criterion is selected as the low-frequency fusion rule and the neighbourhood variance weighted average criterion is selected as the high-frequency fusion rule. Lastly, inverse DFRWT processing is carried out to obtain the composite image. The validity and advantages of our proposed method are verified through simulation results in visual qualities and quantitative evaluation.

A DWT Based Approach for Image Steganography

In this paper we propose a new steganography technique which embeds the secret messages in frequency domain. According to different users' demands on the embedding capa c- ity and image quality, the proposed algorithm is divided into two modes and 5 cases. Unlike the space domain approaches, secret messages are embedded in the high frequency coefficients re- sulted from Discrete Wavelet Transform. Coefficients in the low frequency sub-band are pre- served unaltered to improve the image quality. Some basic mathematical operations are per- formed on the secret messages before embedding. These operations and a well-designed map- ping Table keep the messages away from stealing, destroying from unintended users on the inter- net and hence provide satisfactory security.

Self-adaptive step firefly algorithm based robust watermarking method in DWT-SVD domain

Digital image watermarking, a data hiding method has become widespread to prohibit illegal use of personal files lately. In this study, a robust Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) based technique is proposed for copyright protection. The cover image is firstly decomposed into sub-bands by DWT. Low frequency sub-band is then divided into non-overlapping blocks. Blocks where watermark will be embedded are selected depending on their standard deviation values. Selected blocks are transformed to U, S, and V matrices by SVD. Watermark is embedded into the second row value in the first column of U component obtained by SVD. Embedding scaling factor is determined by Self-Adaptive Step Firefly Algorithm (SASFA) to balance robustness and transparency that are contradictory to each other. Firefly Algorithm (FA) is a simple, easy to implement, and flexible algorithm but it can pass over the global optimum or get trapped at local optimum. Therefore, SASFA, which constitutes the next step of each firefly based on its previous and present situations is used for global exploration of the solution space. Fibonacci-Lucas Transform (FLT) is applied to binary watermark to provide the security of watermarking scheme. Achieved scrambled watermark bits are used in embedding process. Performance of the proposed scheme is measured by using Bit Error Rate (BER), Normalized Correlation (NC), and Peak Signal-to-Noise Ratio (PSNR). Experimental results prove that the method has high robustness level against attacks and a good visual quality.

Image watermarking based on framelet transform

Watermarking is used mostly for multimedia data copyright protection. This paper introduces the Transformation Framelet (FLT) for watermarking digital images. In this technique, the FLT is used to create an original image and watermark the low (LL) frequency subband of an original image in LL subband of an image watermarking. Simulation results demonstrate, in terms of a wide range of traditional attaches, that this approach is imperceptible and stable. MATLAB software version 2014 was used for simulation. Experiments on gray scale and colored images were performed in this work.

Infrared and visible image fusion based on saliency detection and two-scale transform decomposition

In this paper, a two-scale transform-based fusion model for visible and infrared images is proposed to integrate thermal target regions from infrared images and preserve pleasing background details from visible images. To reduce the dependency on multi-scale transform (MST) and improve efficiency, an average filter is initially used to decompose source images into a low-frequency sub-band retaining fine-scale features in small details and a high-frequency sub-band preserving high-contrast variation in intensity. A saliency detection fusion rule based on average filter and median filter is applied on high-frequency fusion to generate saliency maps. After obtaining the saliency maps, the strategy of Weighted Least Square optimization (WLS) is used to supplement visual details and reduce redundancy information to get the final fused detail layer. Meanwhile, we propose a low-frequency fusion rule, a saliency detection algorithm based on infrared thermal information enhancement and visible background detail conservation, which has good performance on maintaining brightness in infrared images and is able to capture background information in visible images. Finally, the fused image shows superior performance compared with nine advanced fusion algorithms in whether subjective or objective evaluations. Quantitative and qualitative experiments on 31 pairs of source images demonstrates that the image fused by the proposed method has abundant texture details, highlighted target regions, and high-quality non-target areas.

An efficient image segmentation and classification of lung lesions in pet and CT image fusion using DTWT incorporated SVM

Automatic segmentation and classification of fused lung Computed Tomography (CT) and Positron Emission Tomography (PET) images is presented. This system consists of four basic stages: 1). Lung image fusion process; 2). Segmentation of fused lung CT/PET images; 3). Post pre-processing; 4). Classification of fused lung images. In the first step, the lung image fusion process is made by deep learning method. At first, the input CT/PET images are decomposed by Dual Tree m-band Wavelet Transform (DTWT). The coefficients of DTWT are fused by deep learning method. This fused image of CT/PET is the input for the following steps. In the next step, the fused CT/PET images are decomposed by DTWT. It produces lower and higher frequency sub-band coefficients. Then the lower frequency components are set to zero. Then higher frequency components are used for reconstruction. Then the clustering-based thresholding method is used for segmentation. In post pre-processing step, the unwanted small regions are removed by morphological operations. Then the lung region is detected. At last, in the classification step, the features are extracted by the intensity and texture-based features. These features are classified by hybrid classifiers like Support Vector Machine (SVM) are used. The performance of the system has a higher classification accuracy of 99% using SVM classifier.

Medical image fusion based on convolutional neural networks and non-subsampled contourlet transform

Although many powerful convolutional neural networks (CNN) have been applied to various image processing fields, due to the lack of datasets for network training and the significant different intensities of diverse multi-modal source images at the same location, CNN cannot be directly used for the field of medical image fusion (MIF), which is a major problem and limits the development of this field. In this article, a novel multimodal medical image fusion method based on non-subsampled contourlet transform (NSCT) and CNN is presented. The proposed algorithm not only solves this problem, but also exploits the advantages of both NSCT and CNN to obtain better fusion results. In the proposed algorithm, source multi-modality images are decomposed into low and high frequency subbands. For high frequency subbands, a new perceptual high frequency CNN (PHF-CNN), which is trained in the frequency domain, is designed as an adaptive fusion rule. In the matter of the low frequency subband, two result maps are adopted to generate the decision map. Finally, fused frequency subbands are integrated by the inverse NSCT. To verify the effectiveness of the proposed algorithm, ten state-of-the-art MIF algorithms are selected as comparative algorithms. Subjective evaluations by five doctors as well as objective evaluations by seven image quality metrics, demonstrate that the proposed algorithm is superior to the other comparative algorithms in terms of fusing multimodal medical images.

Research on Image Fusion Algorithm Based on NSST Frequency Division and Improved LSCN

Single modal medical images provide limited information and cannot reflect all the details of the relevant tissues, which may lead to misdiagnosis in clinical medicine. Therefore, a medical image fusion algorithm based on non-down-sampling shear wave transform (NSST) is proposed. This algorithm fuses multi-modal medical images, enriches the information of fused images, improves the image quality, and provides a basis for clinical diagnosis. Firstly, the low-frequency sub-band and several high-frequency directional sub-bands are obtained by NSST transformation of the source image, and the structural similarity between sub-bands is evaluated. Then, according to the characteristics of low-frequency sub-band images, for sub-images with high similarity, the regional features are obtained by region energy and variance, and the fusion method is based on region feature weighting. For sub-images with low similarity, two images to be fused are input into the LSCN model respectively by fusing the connection items of the improved LSCN model. The improved L-term replaces the ignition frequency in the traditional PCNN as the output. According to the characteristics of high frequency sub-images, the fusion rule of combining visual sensitivity coefficient and regional energy is adopted for sub-images with high similarity. For sub-images with low similarity, an improved guided filter is used to fuse the sub-images in order to maintain the clear edges of the images. Finally, the image is reconstructed by inverse NSST transform. The experimental results show that the proposed algorithm can obtain better fusion effects in both objective and subjective evaluation. The obtained fusion image has rich information, excellent edge retention characteristics, subjectively clear texture and high contrast and good visual effect.

Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-coupled Neural Network

Multi-source information can be obtained through the fusion of infrared images and visible light images, which have the characteristics of complementary information. However, the existing acquisition methods of fusion images have disadvantages such as blurred edges, low contrast, and loss of details. Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform (NSST). Furthermore, the low-frequency subbands were fused by convolutional sparse representation (CSR), and the high-frequency subbands were fused by an improved pulse coupled neural network (IPCNN) algorithm, which can effectively solve the problem of difficulty in setting parameters of the traditional PCNN algorithm, improving the performance of sparse representation with details injection. The result reveals that the proposed method in this paper has more advantages than the existing mainstream fusion algorithms in terms of visual effects and objective indicators.

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.

Quality Guaranteed ECG Signal Compression Using Tunable-Q Wavelet Transform and Möbius Transform-Based AFD

Long-term electrocardiogram (ECG) signal monitoring necessitates a large amount of memory space for storage, which affects the transmission channel efficiency during real-time data transfer. Using a combination of tunable-Q wavelet transform (TQWT) and adaptive Fourier decomposition (AFD), the proposed work develops a new single-channel ECG signal compression algorithm. The input parameters of TQWT were selected so that the lowest frequency subband contained highest energy along with minimal loss. A new Mobius transform-based AFD was introduced to improve the fidelity, by computing highest energy coefficients using Nevanlinna factorization, with suitable decomposition level. Finally, the lossless compression was performed in polar coordinate of final complex coefficients that significantly improved the compression ratio (CR). The algorithm was tested on “python” programming platform, tested in Raspberry Pi (R-Pi), for real-time data processing, and wireless transmission to cloud server and smartphone devices. The suggested work yielded CR, percent root mean square error (PRD), and PRD normalized (PRDN) of 30.06, 7.80, and 11.62, respectively, after testing on 48 Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) ECG data with 30-min duration. A rigorous quality assessment of the reconstructed signal ensured that there was minimal impact on various characteristic domains in the ECG signal, enhancing its acceptability in medical applications.

Three Stream Network Model for Lung Cancer Classification in the CT Images

Abstract Lung cancer is considered to be one of the deadly diseases that threaten the survival of human beings. It is a challenging task to identify lung cancer in its early stage from the medical images because of the ambiguity in the lung regions. This paper proposes a new architecture to detect lung cancer obtained from the CT images. The proposed architecture has a three-stream network to extract the manual and automated features from the images. Among these three streams, automated feature extraction as well as the classification is done using residual deep neural network and custom deep neural network. Whereas the manual features are the handcrafted features obtained using high and low-frequency sub-bands in the frequency domain that are classified using a Support Vector Machine Classifier. This makes the architecture robust enough to capture all the important features required to classify lung cancer from the input image. Hence, there is no chance of missing feature information. Finally, all the obtained prediction scores are combined by weighted based fusion. The experimental results show 98.2% classification accuracy which is relatively higher in comparison to other existing methods.

Subband Adaptive Image Deblocking Using Wavelet Based Convolutional Neural Networks

In this paper, we propose subband adaptive image deblocking using wavelet based convolutional neural networks (CNNs). We build wavelet based CNNs for image deblocking to achieve subband adaptive reconstruction. First, we perform subband adaptive processing after the discrete wavelet transform (DWT) on the input image. For low frequency subband (LL), we use a simple and effective shallow CNN to restore the low frequency component, while for high frequency subbands (LH, HL, and HH) we utilize multi-kernel convolution to capture multiscale features and restore sparse high frequency components. Then, we conduct mixed convolution of dilated convolution and standard convolution to expand the receptive field while introducing channel and spatial attentions to adjust the proportion of different subbands and spatial coordinates. Various experiments on Classic5 and LIVE1 datasets show that the proposed method successfully recovers sharp edges and clear textures in highly compressed images while removing compression artifacts such as blocking and banding. Moreover, the proposed method achieves comparable state-of-the-art performance on compression artifact removal in terms of both visual quality and quantitative measurements.

A Fusion Method for Atomic Force Acoustic Microscopy Cell Imaging Based on Local Variance in Non-Subsampled Shearlet Transform Domain

Atomic force acoustic microscopy (AFAM) is a measurement method that uses the probe and acoustic wave to image the surface and internal structures of different materials. For cellular material, the morphology and phase images of AFAM reflect the outer surface and internal structures of the cell, respectively. This paper proposes an AFAM cell image fusion method in the Non-Subsampled Shearlet Transform (NSST) domain, based on local variance. First, NSST is used to decompose the source images into low-frequency and high-frequency sub-bands. Then, the low-frequency sub-band is fused by the weight of local variance, while a contrast limited adaptive histogram equalization is used to improve the source image contrast to better express the details in the fused image. The high-frequency sub-bands are fused using the maximum rule. Since the AFAM image background contains a lot of noise, and improved segmentation algorithm based on the Otsu algorithm is proposed to segment the cell region, and the image quality metrics based on the segmented region will make the evaluation more accurate. Experiments with different groups of AFAM cell images demonstrated that the proposed method can clearly show the internal structures and the contours of the cells, compared with traditional methods.