Transform Domain Methods Research Articles

Comprehensive Survey of Image Steganography Systems based on FPGA Implementation

Steganography is the art of concealed communication. The basic idea of steganography is to hide secret data inside a cover media in an undetectable manner. Cover media and Secret data can be any popular digital media such as image, video, audio, or text. Various types of image steganography methods have been proposed according to the cover image domain, i.e. spatial domain and transform domain. The spatial domain methods are easy and simple while the transform domain methods are more complex. However, the transform domain methods are more robust against image processing operations. Besides, they are more secure and less detectable in an unsecured channel. The recent steganography methods are based on sophisticated algorithms that include several computational time-consuming tasks, and hence they are unable to embed and extract the hidden data in real-time. Therefore, the trend is to implement the steganography methods in the hardware to speed up their processing time and so improves the efficacy of steganography techniques. FPGA is used in various fields in the modern era due to lower development cost, flexibility, and reconfigurability. Most of the published steganography techniques carried out on FPGA are based on the spatial domain. In this paper, it has comprehensively studied and reviewed various existing implementations of image steganography on FPGAs. This includes studying their general operations, requirements, and performance evaluations. This review would assist researchers in finding the research gaps in FPGA implementation of steganography methods for real-time applications.

Multimodal Medical Image Fusion Utilizing Two-scale Image Decomposition via Saliency Detection.

Modern medical imaging modalities used by clinicians have many applications in the diagnosis of complicated diseases. These imaging technologies reveal the internal anatomy and physiology of the body. The fundamental idea behind medical image fusion is to increase the image's global and local contrast, enhance the visual impact, and change its format so that it is better suited for computer processing or human viewing while preventing noise magnification and accomplishing excellent real-time performance. The top goal is to combine data from various modal images (CT/MRI and MR-T1/MR-T2) into a solitary image that, to the greatest degree possible, retains the key characteristics (prominent features) of the source images. The clinical accuracy of medical issues is compromised because innumerable classical fusion methods struggle to conserve all the prominent features of the original images. Furthermore, complex implementation, high computation time, and more memory requirements are key problems of transform domain methods. With the purpose of solving these problems, this research suggests a fusion framework for multimodal medical images that makes use of a multi-scale edge-preserving filter and visual saliency detection. The source images are decomposed using a two-scale edge-preserving filter into base and detail layers. Base layers are combined using the addition fusion rule, while detail layers are fused using weight maps constructed using the maximum symmetric surround saliency detection algorithm. The resultant image constructed by the presumed method has improved objective evaluation metrics than other classical methods, as well as unhindered edge contour, more global contrast, and no ringing effect or artifacts. The methodology offers a dominant and symbiotic arsenal of clinical symptomatic, therapeutic, and biomedical research competencies that have the prospective to considerably strengthen medical practice and biological understanding.

Recent advances in image dehazing: Formal analysis to automated approaches

Images captured in hazy environments need to be processed to increase their contrast and colour integrity. Dehazing, sometimes referred to as haze removal is an important pre-processing step for image processing and computer vision applications. Numerous methods for dehazing images have been proposed in the literature. This study provides a complete evaluation of numerous image dehazing techniques and their notable standards. This study extracted and presented an important direction of the many algorithms to handle the challenges of dehazing such as model-based methods, transform domain methods, variational-based algorithms, learning-based algorithm and transformer-based algorithms. This study attempts to compile and evaluate the most important studies in the domain of image dehazing. A variety of factors have been considered necessary to provide a detailed information in this study. These factors include datasets that utilised in the literature, challenges faced by the prior researchers, motivations, and recommendations for reducing the drawbacks in the available literature. The systematic rules are utilized for searching all relevant papers on image dehazing using several keyword diversities along with a glance for assessment and the benchmark studies. Image dehazing, which generally eliminates undesirable pictographic effects is often considered as an image enhancement method. A completely automated process, a valid assessment strategy, and databases based on diverse settings are needed for it to operate under real-time applications. Many relevant studies are conducted in order to achieve these substantial goals. We examined numerous image dehazing methods and assessed the objectivity of the results. The results of our study precisely reflect numerous observations on image dehazing regions in contrast to other review articles. We believe that the findings of the study can be a helpful set of recommendations for professionals looking for a full understanding of image dehazing. In addition, we present a thorough examination of the methods used to evaluate image quality using the full-reference category and the no-reference category. Several standard evaluation metrics are utilised to compare the results of the well-known dehazing techniques. A list of standard haze image datasets is also included in this study so that different dehazing methods can be compared. In our opinion, the findings of this study could act as a valuable guide for experts in quest of a detailed understanding of image dehazing.

Adaptive convolution confidence sieve learning for surface defect detection under process uncertainty

Surface defect detection plays an important role in the quality management of industrial manufacturing processes. Existing detection methods are developed based on clean and uncontaminated training data, less considering the process uncertainty. This paper proposes an adaptive convolution confidence sieve learning method for surface defect detection under process uncertainty. First, an edge-separated wavelet transform method combines the spatial and transform domain methods to estimate image noise. After that, an adaptive convolution model integrates parallel multiple convolution kernels, which can cover the defect feature variation. Then, a confidence sieve learning scheme is developed to filter the label noise, and the adaptive convolution is fine-tuned on the high-confidence data to achieve better detection accuracy. Finally, experimental studies on three industrial datasets have verified that the edge-separated wavelet transform method can accurately predict image noise. The adaptive convolution has better representation capability, and the confidence sieve learning improves model robustness.

MLNet: A Multi-Domain Lightweight Network for Multi-Focus Image Fusion

Existing multi-focus image fusion (MFIF) methods are difficult to achieve satisfactory results in both fusion performance and rate simultaneously. The spatial domain methods are hard to determine the focus/defocus boundary (FDB), and the transform domain methods are likely to damage the content information of the source images. Moreover, the deep learning-based MFIF methods are usually confronted with low rate due to complex models and enormous learnable parameters. To address these issues, we propose a multi-domain lightweight network (MLNet) for MFIF, which can achieve competitive results in both performance and rate. The proposed MLNet mainly includes three modules, namely focus extraction (FE), focus measure (FM) and image fusion (IF). In the interpretable FE module, the image features extracted by discrete cosine transform-based convolution (DCTConv) and local binary pattern-based convolution (LBPConv) are concatenated and fed into the FM module. DCTConv based on transform domain takes DCT coefficients to construct a fixed convolution kernel without parameter learning, which can effectively capture the high/low frequency content of the image. LBPConv based on spatial domain can achieve structure features and gradient information from source images. In the FM module, a 3-layer 1×1 convolution with a few learnable parameters is employed to generate the initial decision map, which has the properties of flexible input. The fused image is obtained by the IF module according to the final decision map. In terms of quantitative and qualitative evaluations, extensive experiments validate that the proposed method outperforms existing state-of-the-art methods on three public datasets. In addition, the proposed MLNet contains only 0.01M parameters, which is 0.2% of the first CNN-based MFIF method [25].

1-ADM-CNN: A Lightweight In-field Compression Method for Seismic Data

Large-scale seismic acquisition, versatility, flexibility, automation, and scalability are the objectives of future oil and gas exploration technology. An example of emerging technology for seismic monitoring is distributed acoustic sensing (DAS). The significant amount of data produced by DAS is a challenge that necessitates the development of new technologies for its efficient handling and processing. A typical seismic survey, on the one hand, can generate hundreds of terabytes of raw seismic data per day. The demand for wireless seismic data transmission, on the other hand, remains enormous. The massive amount of data transmission from geophones to the on-site data collection center and its storage poses significant challenges. A lightweight compression procedure is required in order to reduce the data traffic and the storage size at the data center without putting an extra burden on a geophone. In this brief, an efficient implementation of a 1D convolutional neural network (CNN) together with 1-bit adaptive delta modulation is presented for in-field seismic data compression. It is worth mentioning here that the training of CNN is done offline on synthetic data set and hence, the proposed approach has potential for real-time implementation. Furthermore, no assumption on the underlying statistics of noise or the seismic signal is imposed and consequently, the proposed method is suitable for a wide range of seismic data. Furthermore, the proposed method works in the time-domain, unlike existing transform-domain methods, making it suitable for quick diagnosis of bad traces at the data center. Simulation results with real data set reveal that the proposed approach achieves a signal-to-noise ratio (SNR) of approximately 30 dB with a compression gain of 35: 1. Finally, significant superiority in terms of compression gain and reconstruction quality is demonstrated when compared to the existing methods.

Conditional Random Field-Guided Multi-Focus Image Fusion.

Multi-Focus image fusion is of great importance in order to cope with the limited Depth-of-Field of optical lenses. Since input images contain noise, multi-focus image fusion methods that support denoising are important. Transform-domain methods have been applied to image fusion, however, they are likely to produce artifacts. In order to cope with these issues, we introduce the Conditional Random Field (CRF) CRF-Guided fusion method. A novel Edge Aware Centering method is proposed and employed to extract the low and high frequencies of the input images. The Independent Component Analysis—ICA transform is applied to high-frequency components and a Conditional Random Field (CRF) model is created from the low frequency and the transform coefficients. The CRF model is solved efficiently with the -expansion method. The estimated labels are used to guide the fusion of the low-frequency components and the transform coefficients. Inverse ICA is then applied to the fused transform coefficients. Finally, the fused image is the addition of the fused low frequency and the fused high frequency. CRF-Guided fusion does not introduce artifacts during fusion and supports image denoising during fusion by applying transform domain coefficient shrinkage. Quantitative and qualitative evaluation demonstrate the superior performance of CRF-Guided fusion compared to state-of-the-art multi-focus image fusion methods.

Blind Image Inpainting with Sparse Directional Filter Dictionaries for Lightweight CNNs

Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of image quality and run time. However, neural network strategies typically lack a theoretical explanation, which contrasts with the well-understood theory underlying model-based methods. In this work, we leverage the advantages of both approaches by integrating theoretically founded concepts from transform domain methods and sparse approximations into a CNN-based approach for blind image inpainting. To this end, we present a novel strategy to learn convolutional kernels that applies a specifically designed filter dictionary whose elements are linearly combined with trainable weights. Numerical experiments demonstrate the competitiveness of this approach. Our results show not only an improved inpainting quality compared to conventional CNNs but also significantly faster network convergence within a lightweight network design. Our code is available at https://github.com/cv-stuttgart/SDPF_Blind-Inpainting .

Multi-focus image fusion with deep residual learning and focus property detection

Multi-focus image fusion methods can be mainly divided into two categories: transform domain methods and spatial domain methods. Recent emerged deep learning (DL)-based methods actually satisfy this taxonomy as well. In this paper, we propose a novel DL-based multi-focus image fusion method that can combine the complementary advantages of transform domain methods and spatial domain methods. Specifically, a residual architecture that includes a multi-scale feature extraction module and a dual-attention module is designed as the basic unit of a deep convolutional network, which is firstly used to obtain an initial fused image from the source images. Then, the trained network is further employed to extract features from the initial fused image and the source images for a similarity comparison, aiming to detect the focus property of each source pixel. The final fused image is obtained by selecting corresponding pixels from the source images and the initial fused image according to the focus property map. Experimental results show that the proposed method can effectively preserve the original focus information from the source images and prevent visual artifacts around the boundary regions, leading to more competitive qualitative and quantitative performance when compared with the state-of-the-art fusion methods.

A Survey of Multi-Focus Image Fusion Methods

As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the methods based on boundary segmentation was put forward as a group of image fusion method. Thus, a novel classification method of image fusion algorithms is proposed: transform domain methods, boundary segmentation methods, deep learning methods, and combination fusion methods. In addition, the subjective and objective evaluation standards are listed, and eight common objective evaluation indicators are described in detail. On the basis of lots of literature, this paper compares and summarizes various representative methods. At the end of this paper, some main limitations in current research are discussed, and the future development of multi-focus image fusion is prospected.

HID: The Hybrid Image Decomposition Model for MRI and CT Fusion.

Multimodal medical image fusion can combine salient information from different source images of the same part and reduce the redundancy of information. In this paper, an efficient hybrid image decomposition (HID) method is proposed. It combines the advantages of spatial domain and transform domain methods and breaks through the limitations of the algorithms based on single category features. The accurate separation of base layer and texture details is conducive to the better effect of the fusion rules. First, the source anatomical images are decomposed into a series of high frequencies and a low frequency via nonsubsampled shearlet transform (NSST). Second, the low frequency is further decomposed using the designed optimization model based on structural similarity and structure tensor to get an energy texture layer and a base layer. Then, the modified choosing maximum (MCM) is designed to fuse base layers. The sum of modified Laplacian (SML) is used to fuse high frequencies and energy texture layers. Finally, the fused low frequency can be obtained by adding fused energy texture layer and base layer. And the fused image is reconstructed by the inverse NSST. The superiority of the proposed method is verified by amounts of experiments on 50 pairs of magnetic resonance imaging (MRI) images and computed tomography (CT) images and others, and compared with 12 state-of-the-art medical image fusion methods. It is demonstrated that the proposed hybrid decomposition model has a better ability to extract texture information than conventional ones.

A Novel Sub-Nyquist FRI Sampling and Reconstruction Method in Linear Canonical Transform Domain

A Novel Sub-Nyquist FRI Sampling and Reconstruction Method in Linear Canonical Transform Domain

A Non-Blind Robust and Impercept Watermarking Using Discrete Cosine Transform and Discrete Wavelet Transform

Non-blind watermarking is a form of watermarking with a watermark image validation process that requires a host image. The use of the transform domain is more robust and imperceptible. The transform domain method is resistant to various forms of digital image attacks. In this study, Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) were selected as watermark insertion algorithms. DCT is faster and more resistant to attacks, especially in image compression attacks, but has lower imperceptibility than DWT. DWT is also known to be resistant to noise attacks, filtering, blurring, cropping, and has high imperceptibility depending on the sub-band selection but is not resistant to image compression attacks. Based on each algorithm's advantages and disadvantages, there is an opportunity to combine it to analyze and compare the insertion results with DCT and DWT itself. To test the results of imperceptibility, we used the Peak Signal to Noise Ratio (PSNR), while to test the robustness, we used Cross-Correlation (CC) and Bit Error Ratio (BER). Without attacks, the PSNR on the proposed method can reach 71 dB. The CC value without attack can reach a perfect value of 1 and BER = 0. The highest attack test result is CC = 1 on the filtering attack. From the various tests we have conducted, it has been proven that the DCT-DWT is more imperceptive and robust than previous studies

A Review on Speckle Noise Reduction Techniques in Ultrasound Medical images based on Spatial Domain, Transform Domain and CNN Methods

Ultrasonography is non-invasive and painless. In Ultrasonography the images are often affected with Speckle noise. It is a multiplicative noise. To help the doctors to identify the abnormalities properly there are several methods to diagnose as speckle is a major problem. This paper gives details about popular spatial domain, transform domain, CNN techniques for despeckling in ultrasound images. Transform domain methods like Wavelet methods, Curvelet methods, Bayes Shrink methods are prominent among many researches. Deep learning based methods are evolving like DnCNN, ECNDNet etc. for efficient despeckling. An overview of the methods is given here with certain measurement parameters like PSNR, MSE.

Exploiting Laguerre transform in image steganography

Steganography, an approach used to conceal information into the digital media, generally works in two domains: spatial and transform. Though spatial domain methods are simpler, but transform domain methods are good at identifying the features which make the end system more secure. In this work, a novel Steganographic scheme based on an integer sequence named Laguerre transform (LT) is proposed. The Cover image is decomposed into non-overlapping m-pixel groups and then each such pixel group is transformed by applying LT. Variable length bits from the secret information are fabricated into the transformed components. A post-embedding adjustment is applied over these components to minimize the distortion. By applying Inverse LT (ILT), the m-pixel groups are re-computed from the resulting adjusted components. Experimental results reveal that disparity between cover and stego-pixels increases as m increases. Proposed scheme offers better stego image and higher payload compared to some state-of-the-art techniques. Code of this method is publicly available here.

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.

Preamble-Based Channel Estimation for OQAM/FBMC Systems With Delay Diversity

Delay diversity (DD) is a low-complexity and flexible transmit diversity technology for coded offset quadrature amplitude modulation based filter bank multicarrier (OQAM/FBMC) transmission systems. However, the introduction of delay parameters in DD-OQAM/FBMC systems leads to increased channel frequency selectivity, which makes the channel estimation problem more complicated. In this paper, we analyze and develop preamble-based channel estimation methods in DD-OQAM/FBMC systems from the perspective of frequency-domain, transform-domain, and time-domain aspects. Besides, channel estimators that can obtain the minimum channel estimation mean square error under least squares (LS) and linear minimum mean square error (LMMSE) criteria are derived according to different levels of channel knowledge. Specifically, the proposed frequency-domain method estimates each channel frequency response individually based on a simplified frequency-domain channel model. The transform-domain method first estimates the equivalent single-input-single-output channel observed at the receiver assuming subchannel flatness, and then uses a window function in the time domain to separate the channels of different transmit antennas. The time-domain method is constructed based on an accurate time-domain channel model to directly estimate the channel impulse response without making any assumption about the subchannel flatness. A series of simulation experiments is conducted to verify the effectivity of these channel estimation methods for DD-OQAM/FBMC systems.

Multi-focus image fusion: A Survey of the state of the art

Multi-focus image fusion is an effective technique to extend the depth-of-field of optical lenses by creating an all-in-focus image from a set of partially focused images of the same scene. In the last few years, great progress has been achieved in this field along with the rapid development of image representation theories and approaches such as multi-scale geometric analysis, sparse representation, deep learning, etc. This survey paper first presents a comprehensive overview of existing multi-focus image fusion methods. To keep up with the latest development in this field, a new taxonomy is introduced to classify existing methods into four main categories: transform domain methods, spatial domain methods, methods combining transform domain and spatial domain, and deep learning methods. For each category, representative fusion methods are introduced and summarized. Then, a comparative study for 18 representative fusion methods is conducted based on 30 pairs of commonly-used multi-focus images and 8 popular objective fusion metrics. All the relevant resources including source images, objective metrics and fusion results are released online, aiming to provide a benchmark for the future study of multi-focus image fusion. Finally, several major challenges remained in the current research of this field are discussed and some future prospects are put forward.

A multi-source image fusion algorithm based on gradient regularized convolution sparse representation

Image fusion based on the sparse representation (SR) has become the primary research direction of the transform domain method. However, the SR-based image fusion algorithm has the characteristics of high computational complexity and neglecting the local features of an image, resulting in limited image detail retention and a high registration misalignment sensitivity. In order to overcome these shortcomings and the noise existing in the image of the fusion process, this paper proposes a new signal decomposition model, namely the multi-source image fusion algorithm of the gradient regularization convolution SR (CSR). The main innovation of this work is using the sparse optimization function to perform two-scale decomposition of the source image to obtain high-frequency components and low-frequency components. The sparse coefficient is obtained by the gradient regularization CSR model, and the sparse coefficient is taken as the maximum value to get the optimal high frequency component of the fused image. The best low frequency component is obtained by using the fusion strategy of the extreme or the average value. The final fused image is obtained by adding two optimal components. Experimental results demonstrate that this method greatly improves the ability to maintain image details and reduces image registration sensitivity.

Requirements for accurate estimation of shear modulus by magnetic resonance elastography: A computational comparative study

BackgroundMagnetic resonance (MR) elastography is a non-destructive method of measuring biological tissue and is conducive to the early detection of tumors. Researchers usually set different assumptions according to different research objects, then establish and solve wave equations to estimate the shear modulus. Establishing a more reasonable model for a measured object estimates a more accurate shear modulus. Different assumptions of the mathematical model, and the method used to solve the wave equation causes deviation of the estimation. ObjectiveThis study focused on shear modulus deviations caused by differences in calculation methods. The author demonstrated a method to ensure that the measuring range of the selected reconstruction algorithm with selected drive frequency covers the elasticity range of the target tissue. It is hoped to arouse the interest of researchers to introduce new transform domain methods to the field of MR elastography. MethodIn linear, isotropic and local homogeneity assumptions, the typical representative of two different calculation methods are algebraic inversion of the differential equation (AIDE) algorithm and local frequency elastography (LFE) algorithm. To compare the accuracy of these calculation methods, the author adopted a digital phantom that can set the parameter values accurately. It is assumed that the phantom tissue was linear and isotropic, and that the driving wave was sinusoidal. The displacement distribution of waves in the tissue was calculated by the finite element simulation method in two different resolutions with the signal-to-noise ratio (SNR) set to 40 dB and the threshold of relative mean error (RME) no more than 10%. The wavelength-to-pixel-size ratios of the two methods under the setting threshold of RME were compared. ResultsThe lower threshold of wavelength-to-pixel-size ratio for AIDE was close to 10, while that for LFE was nearly 2 (the limitation of Shannon's law) under the setting precision. Thus, the measuring range of the AIDE method was less than that of LFE at the same experimental conditions. ConclusionThe driving frequency selection range of the spatial frequency domain method is wider than that of the spatial domain method. It is worthwhile for researchers to devote more time to introducing new transformation domain method for MR elastography.