Low-frequency Image Research Articles

Frequency division denoising algorithm based on VIF adaptive 2D-VMD ultrasound image.

Ultrasound imaging has developed into an indispensable imaging technology in medical diagnosis and treatment applications due to its unique advantages, such as safety, affordability, and convenience. With the development of data information acquisition technology, ultrasound imaging is increasingly susceptible to speckle noise, which leads to defects, such as low resolution, poor contrast, spots, and shadows, which affect the accuracy of physician analysis and diagnosis. To solve this problem, we proposed a frequency division denoising algorithm combining transform domain and spatial domain. First, the ultrasound image was decomposed into a series of sub-modal images using 2D variational mode decomposition (2D-VMD), and adaptively determined 2D-VMD parameter K value based on visual information fidelity (VIF) criterion. Then, an anisotropic diffusion filter was used to denoise low-frequency sub-modal images, and a 3D block matching algorithm (BM3D) was used to reduce noise for high-frequency images with high noise. Finally, each sub-modal image was reconstructed after processing to obtain the denoised ultrasound image. In the comparative experiments of synthetic, simulation, and real images, the performance of this method was quantitatively evaluated. Various results show that the ability of this algorithm in denoising and maintaining structural details is significantly better than that of other algorithms.

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

Low-frequency conductivity tensor imaging with a single current injection using DT-MREIT

Diffusion tensor-magnetic resonance electrical impedance tomography (DT-MREIT) is an imaging modality to obtain low-frequency anisotropic conductivity distribution employing diffusion tensor imaging and MREIT techniques. DT-MREIT is based on the linear relationship between the conductivity and water self-diffusion tensors in a porous medium, like the brain white matter. Several DT-MREIT studies in the literature provide cross-sectional anisotropic conductivity images of tissue phantoms, canine brain, and the human brain. In these studies, the conductivity tensor images are reconstructed using the diffusion tensor and current density data acquired by injecting two linearly independent current patterns. In this study, a novel reconstruction algorithm is devised for DT-MREIT to reconstruct the conductivity tensor images using a single current injection. Therefore, the clinical applicability of DT-MREIT can be improved by reducing the total acquisition time, the number of current injection cables, and contact electrodes to half by decreasing the number of current injection patterns to one. The proposed method is evaluated utilizing simulated measurements and physical experiments. The results obtained show the successful reconstruction of the anisotropic conductivity distribution using the proposed single current DT-MREIT.

A New Robust Adaptive Fusion Method for Double-Modality Medical Image PET/CT

A new robust adaptive fusion method for double-modality medical image PET/CT is proposed according to the Piella framework. The algorithm consists of the following three steps. Firstly, the registered PET and CT images are decomposed using the nonsubsampled contourlet transform (NSCT). Secondly, in order to highlight the lesions of the low-frequency image, low-frequency components are fused by pulse-coupled neural network (PCNN) that has a higher sensitivity to featured area with low intensities. With regard to high-frequency subbands, the Gauss random matrix is used for compression measurements, histogram distance between the every two corresponding subblocks of high coefficient is employed as match measure, and regional energy is used as activity measure. The fusion factor d is then calculated by using the match measure and the activity measure. The high-frequency measurement value is fused according to the fusion factor, and high-frequency fusion image is reconstructed by using the orthogonal matching pursuit algorithm of the high-frequency measurement after fusion. Thirdly, the final image is acquired through the NSCT inverse transformation of the low-frequency fusion image and the reconstructed high-frequency fusion image. To validate the proposed algorithm, four comparative experiments were performed: comparative experiment with other image fusion algorithms, comparison of different activity measures, different match measures, and PET/CT fusion results of lung cancer (20 groups). The experimental results showed that the proposed algorithm could better retain and show the lesion information, and is superior to other fusion algorithms based on both the subjective and objective evaluations.

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.

Translation between High- and Low-frequency SAR Images using Cycle-Consistent Conditional Adversarial Network

The bi-frequency (high- and low) synthetic aperture radar (SAR) images cannot be directly compared due to their distinct statistical properties. To diminish their statistical difference, we manage to translate the bi-frequency SAR images into one another. Therefore, we propose a cycle-consistent conditional adversarial network to achieve the goal. The cycle-consistency criteria in the Cycle GAN and the conditional generation adversarial networks in the Pix2Pix are integrated to construct the cycle-consistent conditional adversarial network. Experiments on Ku-band and P-band SAR images validate that our method outperforms Cycle GAN and Pix2Pix.

Light Weight IBP Deep Residual Network for Image Super Resolution

Single -image super resolution (SR) is used to reconstruct a high-resolution image with more high-frequency details based on a low-resolution image as input. In recent years, image SR reconstruction based on deep learning methods has shown a considerably better performance than traditional methods. Early deep-learning-based methods deepen convolutional layers and directly reconstruct high-resolution images with complex neural networks. However, with the stacking of modules, network depth and model parameters increase, thereby raising computational resource; hence, it is difficult to apply on low-configuration devices. Furthermore, existing methods ignore the high-frequency details of the image, resulting in unsatisfactory performance. To solve these problems, a lightweight network model that applies the iterative back projection (IBP) mechanism to the network and reduces the dimensionality of the input image features is proposed. The proposed network model consists of three parts, namely, entrance module, main body module, and exit module. It designs a lightweight modular design to reduce the model calculation and control the network depth more easily by adjusting the number of ADB modules. The main part of the model consists of four lightweight accelerating deep residual back projection (ADB) modules. Each ADB module initially decreases the dimensionality of the input image features through a 1×1 convolutional layer to reduce the amount of calculation. Then, IBP is used to back project the image iteratively. Each ADB module only performs the downsampling back projection operation because the image features become larger after upsampling. Through three iterative downsampling back projection units, the high-frequency features of the image are fully explored, and the output image features are then compared with the shallow layer. Image feature fusion is used as input of the next ADB module, and the output part combines the high- and low-frequency image feature output by multiple ADB modules to complete the upsampling by using the PixelShuffle method to generate high-resolution images. Several experiments confirm that the proposed algorithm achieves better SR reconstruction accuracy with faster reconstruction speed than existing image SR methods.

Detection and Imaging of Magnetic Field in the Low-Frequency Regime Using a Ferromagnetic Thin Film Coated With a Thermo-Fluorescent Layer

A novel and original method of low-frequency magnetic field imaging is presented, that uses a magnetic thin film coated with fluorescent dye. Due to high permeability and consequent hysteresis losses, the film is heated by the field. As the fluorescence of the coated material is temperature-dependent, magnetic field mapping can be obtained in a short time and on a large surface by recording images with an S-CMOS camera. This principle had been previously presented in the microwave regime, for both electric and magnetic fields imaging; here, we propose first results in the low-frequency regime (kilohertz frequency range).

An efficient fusion algorithm based on hybrid multiscale decomposition for infrared-visible and multi-type images

To effectively improve the fusion efficiency under the premise of ensuring high fusion quality, a novel and efficient hybrid multiscale decomposition fusion algorithm based on support value transform (SVT) and morphology is proposed. Firstly, each source image is decomposed into one low-frequency approximate background image and a series of high-frequency support value images with salient details by SVT. The decomposition can ensure the support value image in each resolution is unique and the decomposed coefficients can better represent the saliency of image details. Secondly, to overcome the drawback of single-scale decomposition cannot extract details adequately, the idea of constructing dual-channel multiscale morphological decomposition based on à trous algorithm is proposed. Then the low-frequency approximate background image is decomposed by dual-channel multiscale morphological top-bottom hat (TBH) decomposition to extract the bright-dark information. For the high-frequency support value images, the dual-channel multiscale morphological inner-outer gradient (IOG) decomposition is constructed to extract the edge details. Finally, the decomposed coefficients are integrated and the fused image is reconstructed by corresponding inverse transforms. Experiments were conducted on infrared-visible, multi-focus and medical images and the results shown that the proposed method has state-of-the-art fusion performance. Moreover, compared with some advanced algorithms such as NSCT and NSST, the proposed algorithm not only has best fusion stability, but also greatly reduces the fusion processing time under the premise of guaranteeing high fusion quality. In addition, the powers of de-noising in the fusion of noised images and removing edge-halo are also commendable.

Reaching small scales with low-frequency imaging: applications to the Dark Ages.

The initial conditions for the density perturbations in the early Universe, which dictate the large-scale structure and distribution of galaxies we see today, are set during inflation. Measurements of primordial non-Gaussianity are crucial for distinguishing between different inflationary models. Current measurements of the matter power spectrum from the cosmic microwave background only constrain this on scales up to k ∼ 0.1 Mpc-1. Reaching smaller angular scales (higher values of k) can provide new constraints on non-Gaussianity. A powerful way to do this is by measuring the HI matter power spectrum at [Formula: see text]. In this paper, we investigate what values of k can be reached for the Low-Frequency Array (LOFAR), which can achieve [Formula: see text]1″ resolution at approximately 50 MHz. Combining this with a technique to isolate the spectrally smooth foregrounds to a wedge in [Formula: see text]-k⊥ space, we demonstrate what values of k we can feasibly reach within observational constraints. We find that LOFAR is approximately five orders of magnitude away from the desired sensitivity, for 10 years of integration time. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades'.

Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks.

In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed. The study suggests that besides improving the visual contrast of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment. These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking.

Low-frequency source imaging in an acoustic waveguide

Time-harmonic far-field source array imaging in a two-dimensional waveguide is analyzed. A low-frequency situation is considered in which the diameter of the waveguide is slightly larger than the wavelength, so that the waveguide supports a limited number of guided modes, and the diameter of the antenna array is smaller than the wavelength, so that the standard resolution formulas in open media predict very poor imaging resolution. A general framework to analyze the resolution and stability performances of such antenna arrays is introduced. It is shown that planar antenna arrays perform better (in terms of resolution and stability with respect to measurement noise) than linear (horizontal or vertical) arrays and that vertical linear arrays perform better than horizontal arrays, for a given diameter. However a fundamental limitation to imaging in waveguides is identified that is due to the form of the dispersion relation. It is intrinsic to scalar waves, whatever the complexity of the medium and the array geometry.

Distortions Imposed by Ionospheric Faraday Rotation Dispersion in Low-Frequency Full-Polarimetric SAR Images

Spaceborne polarimetric synthetic aperture radar (Pol-SAR) systems operating at low frequencies, such as P-band, are significantly influenced by ionospheric Faraday rotation (FR). A novel theoretical model of the effect of FR dispersion on Pol-SAR images is proposed. Three components of the range compression response are derived with the main function accompanied by two accessories. Simulation results indicate that two accessory functions in existence of FR dispersion can be comparable with the main for P-band ultrawideband systems. It can not only lead to range imaging deteriorations but also bring about extra polarimetric distortions. Finally, an airborne P-band Pol-SAR real scene is used for validation.

Low-frequency dominant electrical conductivity imaging of in vivo human brain using high-frequency conductivity at Larmor-frequency and spherical mean diffusivity without external injection current

Diffusion weighted imaging based on random Brownian motion of water molecules within a voxel provides information on the micro-structure of biological tissues through water molecule diffusivity. As the electrical conductivity is primarily determined by the concentration and mobility of ionic charge carriers, the macroscopic electrical conductivity of biological tissues is also related to the diffusion of electrical ions. This paper aims to investigate the low-frequency electrical conductivity by relying on a pre-defined biological model that separates the brain into the intracellular (restricted) and extracellular (hindered) compartments. The proposed method uses B1 mapping technique, which provides a high-frequency conductivity distribution at Larmor frequency, and the spherical mean technique, which directly estimates the microscopic tissue structure based on the water molecule diffusivity and neurite orientation distribution. The total extracellular ion concentration, which is separated from the high-frequency conductivity, is recovered using the estimated diffusivity parameters and volume fraction in each compartment. We propose a method to reconstruct the low-frequency dominant conductivity tensor by taking into consideration the extracted extracellular diffusion tensor map and the reconstructed electrical parameters. To demonstrate the reliability of the proposed method, we conducted two phantom experiments. The first one used a cylindrical acrylic cage filled with an agar in the background region and four anomalies for the effect of ion concentration on the electrical conductivity. The other experiment, in which the effect of ion mobility on the conductivity was verified, used cell-like materials with thin insulating membranes suspended in an electrolyte. Animal and human brain experiments were conducted to visualize the low-frequency dominant conductivity tensor images. The proposed method using a conventional MRI scanner can predict the internal current density map in the brain without directly injected external currents.

Going Low in a World Going High: The Physiologic Use of Lower Frequency Electron Paramagnetic Resonance.

Yakov Sergeevich Lebedev was a pioneer in high frequency EPR, taking advantage of the separation of g-factor anisotropy effects from nuclear hyperfine splitting and the higher frequency molecular motion sensitivity from higher frequency measurements8. This article celebrates a second EPR subfield in which Prof. Lebedev pioneered, EPR imaging. 9 We celebrate the clinical enhancements that are suggested in this low frequency work and imaging application to animal physiology at lower-than-standard EPR frequencies.

Double‐image asymmetric cryptosystem using cylindrical diffraction and spectrum fusion and compression

A double-image asymmetric cryptosystem is proposed by using cylindrical diffraction and spectrum fusion and compression in the zigzag-scanned domain of discrete cosine transform (DCT). First, each plaintext of double image is converted into spectrum coefficients (SCs) by DCT, and the SCs are zigzag scanned. Second, the first 1/4 of SCs is resized to two low-frequency images, the second 1/4 of SCs is resized to two high-frequency images, while the last 1/2 of SCs is discarded. Third, the two low-frequency and two high-frequency images are fused and merged into three rounds of encryption process by using the complex combination, random phase encoding, and phase reserving–truncation in the cylindrical diffraction transform domain. Finally, the plaintexts of the double image are successfully encrypted into a ciphertext and three private keys with each only 1/8 and totally 1/2 of the size of the plaintext, which gains a high compression efficiency. In the proposed cryptosystem, the quality of the reconstructed image is improved due to the compression in the zigzag-scanned DCT domain, and the security is improved owing to the asymmetric cylindrical diffraction and spectrum-fusion algorithm. The effectiveness and flexibility of the proposed cryptosystem have been validated by the numerical simulation results.

Colour level set regularization for the electromagnetic imaging of highly discontinuous parameters in 3D

In this paper, we propose a novel reconstruction scheme for the low-frequency near-field electromagnetic imaging of high-contrast conductivity distributions inside shielded regions using the system of Maxwell's equations in 3D. In our novel scheme, we focus on estimating the shape characteristics of the electrical conductivity profile inside these regions from low-frequency electromagnetic data measured at external locations for a single frequency. We introduce a colour level set regularization scheme which is a shape-based approach focusing on the simultaneous reconstruction of several shape-like distributions of different conductivity values in the same region of interest. Using two numerical experiments addressing a three-value reconstruction problem related to the imaging of shielded boxes or cargo containers, we compare this novel approach with results obtained from standard voxel-based reconstruction schemes on the one hand and the more established two-value shape-based approach on the other hand. We demonstrate that, depending on the particular situation of the imaging setup, this three-value (or in general multiple-value) shape-based reconstruction technique has the potential to provide superior reconstruction results in many situations, in particular regarding reconstruction of the correct shapes. We also discuss particular challenges of this novel methodology.

A robust video watermarking scheme based on Laplacian pyramid, SVD, and DWT with improved robustness towards geometric attacks via SURF

In this paper, a new robust and secure video watermarking scheme based on discrete Wavelet Transform (DWT), singular value decomposition (SVD), and Laplacian pyramid is proposed for copyright protection applications. The main contributions of this work are: 1) Increasing watermark robustness towards common signal processing attacks by separating the watermark image into a high-frequency image and a low-frequency image using laplacian pyramid. Then, the singular values of each of the low-frequency watermark image and the high-frequency watermark image are hidden in two different DWT subbands of the original video frame. Separating the watermark and hiding its two parts in two different subbands of the DWT of the video frame makes it robust towards noise, filtering, and compression attacks. 2) Improving the quality of the extracted watermarks via correcting the received watermarked videos using SURF points when those videos are geometrically attacked, mainly by rotation, scaling, and shear attacks. 3) Increasing security and avoiding false positive problems (FPP) associated with SVD by using perceptual image hashing in the proposed scheme. Through experimental results and comparisons to other techniques, the proposed scheme has shown very high imperceptibility for the tested watermarked videos in terms of peak signal to noise ratio (PSNR). Also, the proposed scheme has demonstrated high watermark robustness towards various types of attacks, and an improvement in the quality of the extracted watermarks from 4 to 20 dB compared to other techniques.

High Frequency Ultrasound Image Recovery Using Tight Frame Generative Adversarial Networks.

In ultrasound imaging, there is a trade-off between imaging depth and axial resolution because of physical limitations. Increasing the center frequency of the transmitted ultrasound wave improves the axial resolution of resulting image. However, High Frequency (HF) ultrasound has a shallower depth of penetration. Herein, we propose a novel method based on Generative Adversarial Network (GAN) for achieving a high axial resolution without a reduction in imaging depth. Results on simulated phantoms show that a mapping function between Low Frequency (LF) and HF ultrasound images can be constructed.

Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.