Wavelet Coefficients Of Image Research Articles

A new approach based on 2D wavelet transform in the analysis of wettability behavior and evaporation rate for the surface of PTFE dielectric material used in high voltage applications

In this study, a new approach based on 2D wavelet transform is proposed for the analysis of wettability behavior and evaporation rate. Droplet images were taken from the PTFE dielectric sample surface at 30th, 270th, 510th, 750th, 990th, 1230th, 1470th, and 1710th seconds. The droplet images were decomposed into sub-bands using functions from six different wavelet families. The energy, entropy, mean, standard deviation, skewness, and kurtosis measures were calculated using wavelet coefficients for each sub-band image. Linear, exponential, and polynomial fitting methods were used to estimate the contact angle of the droplet images. The energy and standard deviation values, which are the selected optimal measures, were used as input in the fitting methods, and the regression coefficient (R2) and Root Mean Square Error (RMSE) values of the fitting methods were calculated. Thus, a high-performing contact angle prediction model with 2nd level sub-band image wavelet coefficients has been obtained.

A visually meaningful image encryption algorithm based on P-tensor product compressive sensing and newly-designed 2D memristive chaotic map

The noise-like visual feature of cipher images produced by using the traditional image encryption technology explicitly reflects the presence of secret information. To overcome this issue, a visually meaningful image encryption algorithm is proposed based on a newly designed 2D memristive chaotic map, P-tensor product compressive sensing (PTP-CS) and discrete Hartley transform (DHT). For concreteness, a new two-dimensional discrete memristive chaotic map is first designed to provide highly unpredictable secret code streams while maintaining low time consumption. Second, the threshold processing and zigzag confusion operations are performed on the discrete wavelet coefficients of the plain image to meet the prerequisites for effective compression. Third, the intermediate secret information is obtained by utilizing PTP-CS in the compression layer. Information entropy and edge entropy are employed to adaptively identify the complex regions that are suitable for embedding due to inconspicuous visual degradation in the carrier image. Finally, the embedding of the secret information in the DHT domain of these regions is accomplished. Security test and performance analysis confirm that our algorithm has the advantage of a high balance between the encryption security and the decryption recovery, and exhibits excellent performance in important indicators such as visual quality, robustness and timeliness.

Robust quantitative susceptibility mapping via approximate message passing with parameter estimation.

For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps. From a Bayesian perspective, the image wavelet coefficients are approximately sparse and modeled by the Laplace distribution. The measurement noise is modeled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recovered using approximate message passing (AMP). We compare our proposed AMP with built-in parameter estimation (AMP-PE) to the state-of-the-art L1-QSM, FANSI, and MEDI approaches on the simulated and in vivo datasets, and perform experiments to explore the optimal settings of AMP-PE. Reproducible code is available at: https://github.com/EmoryCN2L/QSM_AMP_PE. On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE, deviation from calcification moment and the highest SSIM, while MEDI achieved the lowest high-frequency error norm. On the in vivo datasets, AMP-PE is robust and successfully recovers the susceptibility maps using the estimated parameters, whereas L1-QSM, FANSI and MEDI typically require additional visual fine-tuning to select or double-check working parameters. AMP-PE provides automatic and adaptive parameter estimation for QSM and avoids the subjectivity from the visual fine-tuning step, making it an excellent choice for the clinical setting.

Brain Tumor Detection Through Modified Optimization Algorithm by Region-based Image Fusion

This article is about the fusion of Brain images which are having different features. This article addresses the problems raised in pixel-level image fusion,such as blurring and artifacts caused by the unwanted addition of noise components in fused images. We extracted the regions of the brain image with the proposed tested optimal thresholding-based segmentation by optimizing thresholds with the proposed Sine function adapted improved Whale Optimization Algorithm (SiWOA) algorithm. We named the proposed fusion as SiWOA-FUSION. These optimal segmented regions and discrete wavelet coefficients of images are fused based on interval type - 2 fuzzy rules. Finally, combined image visual quality is optimized with SiWOA by assuming the amount of the correlation of differences (ACD) as an objective function. Experiments are tested on standard benchmark databases and proved better than existing methods.

Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors

The goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel. Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models. However, the blur kernel estimation methods based on sparse priors lack of the consideration for the brightness information about the blur kernel, which will affect the restoration effect of the blur kernel. Besides, previous methods seldom apply sparse priors to both spatial domain and transform domain information. We propose a novel blind text image deblurring model based on multi-scale fusion and sparse priors. Besides the sparse gradient prior on the latent clean text image, we add the sparse prior on the high-frequency wavelet coefficients of the latent text image, which will better constrain the solution space and obtain good clean images. The semi-quadratic splitting method is used to alternately optimize the blur kernel and the latent clean image. Meanwhile, we consider the influence of the brightness feature of the restored blur kernel. By multi-scale fusion technique on the basis of Laplacian weight and saliency weight, we fuse the computed blur kernels in three channels to improve the quality of blur kernel. The experimental results show that our algorithm has good results in the restoration of blur kernels and text images.

Wavelet-packets for deepfake image analysis and detection

As neural networks become able to generate realistic artificial images, they have the potential to improve movies, music, video games and make the internet an even more creative and inspiring place. Yet, the latest technology potentially enables new digital ways to lie. In response, the need for a diverse and reliable method toolbox arises to identify artificial images and other content. Previous work primarily relies on pixel-space convolutional neural networks or the Fourier transform. To the best of our knowledge, synthesized fake image analysis and detection methods based on a multi-scale wavelet-packet representation, localized in both space and frequency, have been absent thus far. The wavelet transform conserves spatial information to a degree, allowing us to present a new analysis. Comparing the wavelet coefficients of real and fake images allows interpretation. Significant differences are identified. Additionally, this paper proposes to learn a model for the detection of synthetic images based on the wavelet-packet representation of natural and generated images. Our forensic classifiers exhibit competitive or improved performance at small network sizes, as we demonstrate on the Flickr Faces High Quality, Large-scale Celeb Faces Attributes and Large-scale Scene UNderstanding source identification problems. Furthermore, we study the binary Face Forensics++ (ff++) fake-detection problem.

Flexible infrared images destriping algorithm with L1-based sparse regularization for wide-field astronomical images

A novel infrared image destriping method is proposed from a single image. The proposed method can estimate the clean image and stripe noise image simultaneously. Firstly, we analyze the common challenges in current destriping algorithms, and then integrate an unidirectional total variation constraint to remove stripe noise effectively. Wavelet transform is introduced to constrain the spatial mixture Gaussian noise in the degraded images. We employ the total variation regularization to smooth the distribution of Wavelet coefficients. Namely, the distribution of Wavelet coefficients of clean image is sparser than that of degraded stripe image. Finally, an efficient optimization strategy is described to alternately estimate the clean image and stripe image until convergence. Compared experiments are carried out on the simulated and real infrared images with the state-of-the-art models. The proposed method is able to recover high resolution infrared image in low computation cost.

A probabilistic Bayesian approach to recover map and phase images for quantitative susceptibility mapping.

Undersampling is used to reduce the scan time for high-resolution three-dimensional magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover map and phase images for quantitative susceptibility mapping (QSM), while allowing automatic parameter estimation from undersampled data. Sparse prior on the wavelet coefficients of images is interpreted from a Bayesian perspective as sparsity-promoting distribution. A novel nonlinear approximate message passing (AMP) framework that incorporates a mono-exponential decay model is proposed. The parameters are treated as unknown variables and jointly estimated with image wavelet coefficients. Undersampling takes place in the y-z plane of k-space according to the Poisson-disk pattern. Retrospective undersampling is performed to evaluate the performances of different reconstruction approaches, prospective undersampling is performed to demonstrate the feasibility of undersampling in practice. The proposed AMP with parameter estimation (AMP-PE) approach successfully recovers maps and phase images for QSM across various undersampling rates. It is more computationally efficient, and performs better than the state-of-the-art -norm regularization (L1) approach in general, except a few cases where the L1 approach performs as well as AMP-PE. AMP-PE achieves better performance by drawing information from both the sparse prior and the mono-exponential decay model. It does not require parameter tuning, and works with a clinical, prospective undersampling scheme where parameter tuning is often impossible or difficult due to the lack of ground-truth image.

Application of Addition and Multiplication Noise Model Parameter Estimation in INSAR Image Processing

INSAR images are inevitably contaminated by noise during the process of generation, transmission, compression, and reception. Noise not only affects the quality of the INSAR image, but also affects subsequent operations such as the design of corresponding filters, INSAR image segmentation, compression, restoration, and feature recognition. The INSAR image noise model is mainly divided into additive noise and multiplicative noise. Compared with additive noise, multiplicative noise is more complicated due to INSAR image correlation and non-Gaussian. Based on least squares algorithm system of additive and multiplicative mixed noise model, this paper proposes a method of using PCA to remove multiplicative gamma distribution noise. The pure noise coefficient is obtained by subtracting the original coefficient from the diagonal wavelet coefficient of the noisy image, and the mode of the local variance is calculated as the estimation value of the noise standard deviation. Experiments show that the proposed method can obtain more accurate estimation of noise; in particular in the case of less noise and more detailed image information, its effect is more obvious.

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution.

Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.

Speckle filtering of ultrasonic images using weighted nuclear norm minimization in wavelet domain

Medical images such as ultrasound images suffer from multiplicative speckle noise which reduces the contrast of ultrasound images and adversely affects the diagnosis process. Discrete Wavelet transform (DWT) is widely accepted in the area of image processing due to its time localization, multi-resolution and sparseness properties. DWT decomposes the noisy image into approximation coefficients and detail coefficients. The detail wavelet coefficients of clean image have sparsity property, whereas in the case of noisy image, sparsity of detail coefficients is reduced due to the presence of speckle noise. Thus, in the proposed method, low rank based Weighted Nuclear Norm Minimization (WNNM) is applied on detail coefficients to reveal the sparsity property of DWT. WNNM is applied on the group matrix of non-local similar patches of detail subbands of DWT to approximate the low-rank denoised version of the subbands. Moreover, less amount of edge and structure information is present in the approximation coefficients of DWT. Thus, in the proposed method, Non-local Means (NLM) filter with Square-Chord distance is also used to denoise speckle noise from approximation coefficients of DWT. Exhaustive experiments are conducted on various images such as real US images, simulated kidney image and synthetic image. It is observed that the mean improvement in terms of PSNR and CNR values of the proposed hybrid method are 1.50% and 3.81% respectively over WNNM based Despeckling using Low-Rank Approximation (DLRA) method. Qualitative and quantitative analyses show that the proposed hybrid method performed better than existing speckle reduction methods in terms of both speckle reduction and edge preservation.

Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning

Glaucoma is a domineering and irretrievable neurodegenerative eye disease produced by the optical nerve head owed to extended intra-ocular stress inside the eye. Recognition of glaucoma is an essential job for ophthalmologists. In this paper, we propose a methodology to classify fundus images into normal and glaucoma categories. The proposed approach makes use of image denoising of digital fundus images by utilizing a non-Gaussian bivariate probability distribution function to model the statistics of wavelet coefficients of glaucoma images. The traditional image features were extracted followed by the popular feature selection algorithm. The selected features are then fed to the least square support vector machine classifier employing various kernel functions. The comparison result shows that the proposed approach offers maximum classification accuracy of nearly 91.22% over the existing best approaches.

Chaotic Reversible Watermarking Method Based on IWT with Tamper Detection for Transferring Electronic Health Record

Health IoT deals with sensitive medical information of patients, therefore security concerns need to be addressed. Confidentiality of Electronic Health Record (EHR) and privacy are two important security requirements for IoT based healthcare systems. Recently, watermarking algorithms as an efficient response to these requirements is in the spotlight. Further, as smart city-based applications have to react to real-time situations, efficient computation is a demand for them. In this paper, a secure, lightweight, reversible, and high capacity watermarking algorithm with tamper detection capability is proposed for IoT based healthcare systems. The scheme has applied Integer Wavelet Transform (IWT) and chaotic map for efficiency and increasing security. EHR is encrypted and then embedded into the Least Significant Bits (LSB) of wavelet coefficients of medical images. The proposed method has been extensively tested for various color and grayscale commonly used medical and general images. Investigations on experimental results and criterions such as Peak Signal to Noise Ratio (PSNR) and Bit Error Ratio (BER) above 45.41 dB and 0.04, respectively, for payload of 432,538 bits indicate that the proposed method, besides providing security, being reversible, tamper detection capability, and high embedding capacity, has high imperceptibility and adequate resistance against different types of attacks.

Semi-fragile watermarking scheme based on perceptual hash function (PHF) for image tampering detection

The increasing performances of personal computers, as well as software of image processing, enable the easy manipulation of digital media content. Unfortunately, this easy manipulation makes the detection of changes in the multimedia content a very difficult task. Digital watermarking is among the most appropriate techniques to verify the integrity of multimedia content. In this paper, we propose a semi-fragile watermarking scheme for JPEG2000 image self-authentication that ensuring the robustness of the watermark against the compression attacks generated by the JPEG2000 encoder itself. The scheme is combined with the JPEG2000 encoder by embedding the generated watermark into the host image during the JPEG2000 compression process. To generate the watermark we suppose to use a perceptual hash function (PHF) operating on discrete wavelet coefficients of the host image. The proposed watermark generation process leads the system to verify the integrity of the image without the need to any file except for the watermarked image. The watermark is embedded during the JPEG2000 compression process after Discrete Wavelet Transform (DWT) step into the approximation sub-band coefficients of the five wavelet decomposition using Index Modulation Quantification (QIM), and can be extracted during image decoding. To prove the authentication of the image, the system compares the extracted watermark with the new watermark generated from the received image. Experimental results show that our proposed approach has not only an extremely high accuracy of tampering detection but also a relatively very high resistance against JPEG2000 compression attacks.

Multi-Scale Image Super-Resolution Via a Single Extendable Deep Network

Deep neural networks have achieved remarkable success in single image super-resolution (SISR). However, in most cases, image SR with different scale factors is considered as different tasks and solved by training specific models. It makes the image SR applications inefficient and tedious. Hence, to tackle these problems, we propose a lightweight and fast network (MSWSR) to implement multi-scale SR simultaneously by learning multi-level wavelet coefficients of the target image. The proposed network is composed of one CNN part and one RNN part. The CNN part is used for predicting the highest-level low-frequency wavelet coefficients, while the RNN part is used for predicting the rest frequency bands of wavelet coefficients. Moreover, the RNN part is extendable to more scales. For further lightweight, a non-square (side window) convolution kernel is proposed to reduce the network parameters. Experiments on commonly-used datasets demonstrate that the proposed method achieves favorable reconstruction performance with a fast speed and lightweight network. The code is available at https://github.com/FVL2020/MSWSR.

A blind watermarking scheme based on computational ghost imaging in wavelet domain

A secure, robust, and blind watermarking method based on computational ghost imaging (CGI) in the discrete wavelet transform domain is proposed. In this method, an original watermark is firstly encrypted by the CGI system with chaotic key, then the sequences of the ciphertext are quantified into two levels and hidden in the wavelet coefficients of a host image. The quantization on the ciphertext compresses the amount of data to be hidden but does not induce serious deterioration in the retrieved image comparing with the conventional CGI. Simulation results show that the decrypted watermark is still distinguishable and can be authenticated with the nonlinear correlation coefficient. The embedded data can be blindly extracted with no help of the original host image. The chaotic key acts as both the encryption and the embedding key, which guarantee the security of the watermarking scheme.

An Image Denoising Technique using Quantum Wavelet Transform

The amalgamation of ‘Quantum computing’ with image processing represents the various ways of handling images for different purposes. In this paper,an image denoising scheme based on quantum wavelet transform is proposed.A noisy image is embedded into the wavelet coefficients of the original image. As a result,it affects the visual quality of the original image. The quantum Daubechis kernel of 4th order is used to extract wavelet coefficients from the resultant image. Then a quantum oracle is implemented with a suitable thresholding function to decompose the wavelet coefficients into a greater effect applicable for the original image and lower effect for the noisy image wavelet coefficients. However,original image wavelet coefficients are greater than the noisy wavelet coefficients.A detail computational time complexity analysis is given and compared with some state-of-art denoising techniques. The result analysis shows that the proposed quantum image denoising technique has better visual quality in terms of PSNR,MSE and QIFM values Compare to others.

Extremely adaptive image retrieval scheme employing an optimized wavelet technique intended for characterization maps

The demand for adaptive image retrieval is still an active research area, particularly in a dynamic environment. Erstwhile retrieval schemes ensure adaptivity by tuning the same image basis with wavelet transforms, in accordance with user’s significance. To enhance adaptivity and improve the retrieval performance, an Extremely Adaptive Image Retrieval (EAIR) scheme is presented that associates each image with different wavelet basis. This objective is achieved by building Characterization maps from wavelet coefficients of images (query and target) using higher order standardized moments of the Gamma function. The resulting maps are approximated by Volterra Series and later, mathematically programmed by Integral Global Optimization (IGO) algorithm for wavelet adaptation. Finally, the best wavelet filter for each query image is fitted using the Multivariate Adaptive Regression Splines (MARS). Characterization Maps rendered by EAIR achieves 60% reduction in Relative Approximation Error (RAE) with 11% decrease in query time observed under diverse dataset. Also, relative Precision-Recall (P-R), Precision at 5 (P5) analyses reveals a significant retrieval improvement of 9.52%, 1.35%, 1.12%, 8.07% by EAIR on Caltech, Messidor, AT&T, Vistex respectively.

Characterization of local regions for wavelet-based image denoising using a statistical approach

The shape of local window plays a vital role in the estimation of original signal variance, which is used to shrink the noisy wavelet coefficients in wavelet-based image denoising algorithms. This paper presents an anisotropic-shaped region-based Wiener filtering (ASRWF) and BayesShrink (ASRBS) algorithms, which exploit the region characteristics to estimate the original signal variance using a statistical approach. The proposed approach divides the region centered on a noisy wavelet coefficient into various non-overlapping subregions. The Euclidean distance-based measure is considered to obtain the similarities between reference subregion and adjacent subregions. An appropriate threshold value is estimated by applying a statistical approach on these distances and the sets of similar and dissimilar subregions are obtained from a defined region. Thus, an anisotropic-shaped region is obtained by neglecting the dissimilar subregions in a defined region. The variance of every similar subregion is calculated and then averaged to estimate the original signal variance to shrink noisy wavelet coefficients effectively. Finally, the estimated signal variance is utilized in Wiener filtering and BayesShrink algorithms to improve the denoising performance. The performance of the proposed algorithms is analyzed qualitatively and quantitatively on standard images for different noise levels.

A Deep Learning Interpretable Model for Novel Coronavirus Disease (COVID-19) Screening with Chest CT Images

In this article, we propose a convolutional neural network (CNN)-based model, a ResNet-50 based model, for discriminating coronavirus disease 2019 (COVID-19) from Non-COVID-19 using chest CT. We adopted the use of wavelet coefficients of the entire image without cropping any parts of the image as input to the CNN model. One of the main contributions of this study is to implement an algorithm called gradient-weighted class activation mapping to produce a heat map for visually verifying where the CNN model is looking at the image, thereby, ensuring the model is performing correctly. In order to verify the effectiveness and usefulness of the proposed method, we compare the obtained results with that obtained by using pixel values of original images as input to the CNN model. The measures used for performance evaluation include accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and Matthews correlation coefficient (MCC). The overall classification accuracy, F1 score, and MCC for the proposed method (using wavelet coefficients as input) were 92.2%, 0.915%, and 0.839%, and those for the compared method (using pixel values of the original image as input) were 88.3%, 0.876%, and 0.766%, respectively. The experiment results demonstrate the superiority of the proposed method. Moreover, as a comprehensible classification model, the interpretability of classification results was introduced. The region of interest extracted by the proposed model was visualized using heat maps and the probability score was also shown. We believe that our proposed method could provide a promising computerized toolkit to help radiologists and serve as a second eye for them to classify COVID-19 in CT scan screening examination.