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Introduction. Optical coherence tomography (ОКТ) is a non-invasive instrument for studying optically heterogeneous media with micron precision, including skin cancer. However, ОКТ tissue images are very noisy. It complicates both expert and automated image evaluations. There are almost no systematic comparisons of noise reduction algorithms in the literature.Objective. To obtain comparative test results on a set of ОКТ images of skin melanoma using various noise reduction algorithms.Materials and methods. A number of noise reduction algorithms were described, which include two relatively simple classical algorithms: Wiener and median, and more complex ones: a Complex Diffusion Filter (CDF), an Interval type-II Fuzzy Anisotropic Diffusion Filter (ITTFADF) and an Empirical Mode Decomposition (EMD) filter, previously proposed by the author for visualizing of mesh implants. Quantitative metrics were determined: a Signal-to-Noise Ratio (SNR) metrics, an Effective Number of Looks (ENL) metrics, Structural Similarity Index Metrics (SSIM) and a correlation coefficient χ, reflecting two main principles of improving image quality: to reduce noise and to save the borders of tissue layers and heterogeneities.Results. The results of a comparative testing on a set of images, consisting of 10 melanomas (to which various noise reduction algorithms were applied) were obtained.Conclusion. The study did not reveal the best algorithm for all four metrics. According to the SNR metric, the EMD and the CDF filters perform the best depending on the type of area. At the same time, the EMD filter is either the best in all respects, or is inferior in SNR in heterogeneous areas and takes the second place in ENL. Taking as the correct hypothesis that the border preservation is more important before an integral noise estimate, it is possible to make an unambiguous conclusion about the need to use the EMD filter. As an alternative to the EMD filter, Wiener filter (which wins on the border preservation metrics) should be used or the ITTFADF, which ranked third in all used metrics.

Desert seismic events are disturbed and contaminated by strong random noise, which complicates the subsequent processing, inversion, and interpretation of the data. Thus, noise suppression is an important task. The complex characteristics of random noise in desert seismic records differ completely from those of Gaussian white noise such that they are non-stationary, non-Gaussian, non-linear and low frequency. In addition, desert seismic signals and strong random noise generally share the same frequency bands. Such factors bring great difficulties in the processing and interpretation of desert seismic data. To obtain high-quality data in desert seismic exploration, we have developed an effective denoising method for desert seismic data, which performs energy spectrum analysis in the empirical curvelet transform (ECT) domain. The empirical curvelet coefficients are divided into two different groups according to their energy spectrum distributions. In the first group, which contains fewer effective signals, a large threshold is selected to remove lots of random noise; the second group, with more effective signals, a coherence-enhancing diffusion filter (CEDF) is used to eliminate the noise. Unlike traditional curvelet transforms, ECT not only has the multi-scale, multi-direction, and anisotropy properties of conventional curvelet transform, but also provides adaptability to separate the effective signals from the random noise. We examine synthetic and field desert seismic data. The denoising results demonstrate that the proposed method can be used for preserving effective signals and removing random noise.

Tumor in brain is an anthology of anomalous cells. It leads to increase in death rate among humans. Therefore, in this manuscript, a fusion process is proposed to combine structural and texture information of four MRI sequences (T1C, T1, Flair and T2) for the detection of brain tumor. A discrete wavelet transform (DWT) along with Daubechies wavelet kernel is utilized for fusion process which provides a more informative tumor region as compared to an individual single sequence of MRI. After the fusion process, a partial differential diffusion filter (PDDF) is applied to remove noise. A global thresholding method is used for segmenting tumor region which is then fed to proposed convolutional neural network (CNN) model for finally differentiating tumor and non-tumor regions. Five publicly available datasets i.e., BRATS 2012, BRATS 2013, BRATS 2015, BRATS 2013 Leader board and BRATS 2018 are used for proposed method evaluation. The results show that fused images provide better results as compared to individual sequences on benchmark datasets.

This article proposes a variational mode decomposition (VMD)-based preprocessing scheme to regularize a target porosity signal to improve its prediction from seismic attributes. A workflow consisting of preprocessing, prediction, and postprocessing stages is designed to implement the proposed scheme on a real hydrocarbon field dataset. In the preprocessing stage, the original porosity signal is decomposed into multiple intrinsic mode functions (IMFs). The regularized porosity signal is constructed from the decomposed IMFs by removing some of the higher frequency modes. Three alternate methods—empirical mode decomposition, complementary ensemble empirical mode decomposition, and VMD—are used for decomposition to identify the best regularization technique. In each case, normalized mutual information and entropy are used to regulate the filtering. Then, mapping of the porosity signal and seismic data is carried out by support vector regression (SVR). Four metrics—correlation coefficient, root-mean-square error, absolute error mean, and scatter index—are used to quantify the performance of SVR while using original and regularized porosity signals as targets separately. The tuned SVR parameters are then used to predict synthetic porosity logs over the study area from seismic volumes. Finally, the predicted porosity volume is postprocessed using an improved complex adaptive diffusion filter. The performance of the SVR is also compared with an artificial neural network. This framework can be used to predict different lithological properties from seismic attributes and visualize them over a reservoir area.

To overcome the staircase effects and simultaneously avoid edge blurring, here the authors proposed a novel algorithm of adaptive fourth-order partial differential equation (PDE) for image smoothing. The proposed method is based on You and Kaveh (YK) model that first performs a pre-processing using coherence-enhancing diffusion filter before the smoothing. Then, the authors select an adaptive edge-stopping function founded on the Tansig function to effectively remove the noise and insignificant details without destruction of important edges and textures in the image. MATLAB simulation results validate the efficiency of the proposed model quantitatively and visually, when compared with other state-of-the-art methods.

Image denoising is one of important tasks in image processing. Diffusion filter-based methods have been widely used for image denosing. In order to solve the staircase effect introduced by second-order diffusion filter, different fourth-order diffusion filters were proposed, which can achieve good performance on image denoising. However, fourth-order diffusion filter often suffers over-smoothness. In this paper, we propose an anisotropic fourth-order diffusion filter for astronomical image denoising, in which the eigenvalue of Hessian matrix is introduced for preserving edge information. The experimental results of denoising on several astronomical images show that the proposed method can achieve better performance than several other methods.

The concentration of macromolecules in solution is a crucial property in many areas of research, including the development and commercialization of biological therapeutics. For proteins in particular, none of the reported methods for measuring concentration detect a molecular property that is known a priori; rather, they rely on ligand binding, degradation and derivitization, or an intrinsic property that must be determined experimentally. The purpose of this report is to describe (1) a diffusion-filtered qNMR experiment (DF-qNMR) for quantitating macromolecules in complex matrices and (2) an overall method for measuring absolute protein concentration based on this DF-qNMR experiment. This method combines protein denaturation with the diffusion filter to produce clean spectra of the protein with well-resolved resonances, regardless of the matrix complexity. The concentration is then obtained by comparing the peak area of the valine/isoleucine/leucine methyl groups to an external, certified, small-molecule quantitation standard. The method, which is referred to as VILMHA (valine isoleucine leucine methyl hydrogen analysis), was tested on three proteins of various sizes. In all cases, the measured concentration was within 1.8% of the labeled value for the undiluted standard reference material evaluated. In addition, the RSD's were less than 1.25% in all cases and less than 1% in most cases. The accuracy, precision, and ease of use make this method superior to existing absolute protein concentration methods. Furthermore, VILMHA is ideally suited to serve as the basis for converting the relative protein concentration methods into absolute methods or establishing molecular-specific parameters. Finally, DF-qNMR has the potential to quantitate other types of macromolecules (e.g., such as polymers, surfactants, etc.) in the presence of small-molecule contaminants.

Transmembrane water exchange, including intra-to-extravascular and intra-to-extracellular ones, are potential biomarkers in the diagnosis and understanding of cancers, brain disorders, and other diseases. Filter-exchange imaging (FEXI), a special case of diffusion exchange spectroscopy (DEXSY) adapted for clinical applications, has the potential to reveal different physiological water exchange processes using the same MRI sequence. In this study, we aim to explore the feasibility of FEXI in measuring different water exchange processes by modulating the diffusion filter (bf) and detection blocks in FEXI. Two FEXI protocols were implemented on a 3T MRI clinical scanner and reveal distinct apparent exchange rate (AXR) contrast in brain tissues in seven healthy volunteers. AXR estimated from a FEXI protocol with bf ​= ​250 ​s/mm2, which is expected to filter out the vascular water specifically, are significantly larger than those of a FEXI protocol with bf ​= ​900 ​s/mm2. Besides, the filter efficiency of FEXI with bf ​= ​250 ​s/mm2 shows a strong correlation with vascular density, a metric estimated as the fraction of water exhibiting intravoxel incoherent motion (IVIM). AXR of FEXI with bf ​= ​250 ​s/mm2 agrees with the vascular water efflux rate constants reported by other independent measurements, although the physiological basis of the AXR of FEXI with bf ​= ​900 ​s/mm2 is not clear yet. Collectively, our current results demonstrate the feasibility of FEXI in measuring different water exchange processes in vivo, and that FEXI targeting the vascular water could help characterize the intra-to-extravascular water exchange process.

Thermal pattern which shows asymmetry plays a vital role to diagnosis the cancer in breast thermogram. In this work, asymmetrical pattern of thermal images is analysed using coherence-enhanced diffusion filtering (CEDF)-based reaction diffusion level set and Curvelet transform (CT). The breast tissues are segmented using reaction diffusion level set method (RDLSM). Development of edge map generated by CEDF acts as edge indicator in this level set. The regions of left and right breast from the segmented images are separated. The categories of abnormal and normal sets have been established by pathological and healthy conditions from the separated regions. Three levels of Curvelet decomposition are performed on these tissues, and features of texture such as contrast, dissimilarity and difference of variance are determined from the extracted Curvelet coefficients. The results show that coherence-enhanced diffusion filter-based RDLSM is able to segment the regions of breast. This technique shows the correlation between the ground truth and segmented output as high value than the conventional level set. CT offers the directional information as more than wavelet and ridgelet transform. Therefore, grey-level co-occurrence features extracted from the coefficients of CT are noticed to be important in delineating the tissues of normal and abnormal breast. Hence, it seems that this can be used in the automated detection of asymmetry in breast thermograms for efficient diagnosis of breast cancer.

Phased array is one of the most promising ultrasonic testing methods. Non-destructive testing finds its application in various industries, that includes online monitoring purpose, defect identification and so on. Both conventional and non-conventional testing methods are used in industries to carry on the detection process. Ultrasonic phased array testing finds its application in almost all the areas, which is a counterpart for radiography. In this paper, an automated testing method is proposed, which deals with weld flaw segmentation technique using hybrid clustering and Hilbert transform. In order to improve the quality of the obtained image, preprocessing steps are required and adaptive diffusion filter is proposed for removal of noise and pixel correction. In this proposed work, 2D-Hilbert Transform is implemented for the calculation of analytic images and satisfy the properties as in the case of 1D method. This technique provides a 2D spectrum equal to zero in one quadrant. After the process of segmenting the region of interest, the unique features are estimated using the Gray level co-occurrence GLCM. These unique features are fed as the input to deep learning for best classification of the defects in weldments.

Conventional proton nuclear magnetic resonance (1H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional 1H-NMR spectra is difficult. This is because the baseline appearance is masked by the dense and high-intensity signals from small molecular components present in the sample mixtures. In this study, we introduced an integrated analytical strategy based on the combination of additional measurement using a diffusion filter, covariation peak separation, and matrix decomposition in a small-scale training dataset. This strategy is aimed to extract signal profiles of soluble macromolecular components from conventional 1H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional 1H-NMR spectra of water-soluble fish muscle extracts and investigated the distribution characteristics of fish diversity and muscle soluble macromolecular components, such as lipids and collagens. We identified a cluster of fish species with low content of lipids and high content of collagens in muscle, which showed great potential for the development of functional foods. Because this mechanical data processing method requires additional measurement of only a small-scale training dataset without special sample pretreatment, it should be immediately applicable to extract macromolecular signals from accumulated conventional 1H-NMR databases of other complex gelatinous mixtures in foods.

Poor spatial resolution and low signal-to-noise ratio (SNR) along with the finite image sampling constraint make lesion segmentation on Nuclear Medicine (NM) images (e.g., PET–Positron Emission Tomography) a challenging task. Since the size, signal-to-background ratio (SBR) and SNR of lesion vary within and between patients, performance of the conventional segmentation methods are not consistent against statistical fluctuations. To overcome these limitations, a hybrid region growing segmentation method is proposed combining non-linear diffusion filter and global gradient measure (HNDF-GGM-RG). The performance of the algorithm is validated on PET images and compared with the 40%-fixed threshold and a state-of-the-art active contour (AC) methods. Segmented volume, dice similarity coefficient (DSC) and percentage classification error (% CE) were used as the quantitative figures of merit (FOM) using the torso NEMA phantom that contains six different sizes of spheres. A 2:1 SBR was created between the spheres and background and the phantom was scanned with a Siemens TrueV PET-CT scanner. 40T method is SNR dependent and overestimates the volumes (≈4.5 times). AC volumes match with the true volumes only for the largest three spheres. On the other hand, the proposed HNDF-GGM-RG volumes match closely with the true volumes irrespective of the size and SNR. Average DSC of 0.32 and 0.66 and % CE of 700% and 160% were achieved by the 40T and AC methods respectively. Conversely, average DSC and %CE are 0.70 and 60% for HNDF-GGM-RG and less dependent on SNR. Since two-sample t-test indicates that the performance of AC and HNDF-GGM-RG are statistically significant for the smallest three spheres and similar for the rest, HNDF-GGM-RG can be applied where the size, SBR and SNR are subject to change either due to alterations in the radiotracer uptake because of treatment or uptake variability of different radiotracers because of differences in their molecular pathways., Medical imaging; Nuclear medicine; Biomedical engineering; Positron emission tomography (PET); Threshold; Region growing; Active contour; Low signal to background ratio

Infrared moving point target detection is a challenging task in infrared tracking systems. In this paper, an anisotropic spatial-temporal fourth-order diffusion filter (ASTFDF) is proposed for background prediction from infrared frame images. Using it, we can detect infrared point targets by subtracting the predicted background from the original image. Experiments in detecting targets from several different infrared image frame sequences indicated that the proposed ASTFDF can predict the background effectively and that the target can be detected precisely while achieving a higher background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG) compared with state-of-the-art methods.

This paper proposes a diffusion filter based scheme to denoise seismic attributes and to improve the porosity volume, which is predicted from seismic attributes. We compare the performances of multiple diffusion [such as Perona–Malik diffusion filter, complex diffusion filter, improved complex adaptive diffusion filter (ICADF)] and nondiffusion (such as two-dimensional (2-D) median, 3-D median, smoothing, and bilateral filter) based filters in terms of four metrics such as root mean square error (RMSE), normalized RMSE, signal to noise ratio (SNR), and peak SNR (PSNR). In our earlier publication, we used an artificial neural network (ANN) to predict a lithological property (sand fraction) over a study area. We trained the ANN using an integrated dataset of low-resolution seismic attributes and a limited number of high-resolution well logs. In this paper, we generate the porosity volume from the seismic attributes using an ANN. The predicted porosity logs contain irregularities and artifacts due to the nonlinear mapping of the learning algorithm (e.g., ANN). We apply a set of filters to the output of the ANN to regularize the predicted porosity volume. The filtered porosity logs are compared with the generated log. The ICADF has been found to be most suitable for denoising the seismic data and the porosity volume. Generation of porosity maps from seismic inputs would be helpful to petroleum engineers for reservoir characterization.

We determine the conditional expected logarithmic (that is, continuously compounded) return on a stock whose price evolves in terms of the Feller diffusion and then use it to demonstrate how one must know the exact probability density that describes a stock’s return before one can determine the correct way to calculate the abnormal returns that accrue on the stock. We show in particular that misspecification of the stochastic process which generates a stock’s price will lead to systematic biases in the abnormal returns calculated on the stock. We examine the implications this has for the proper conduct of empirical work and for the evaluation of stock and portfolio performance.

The performance of classifier algorithms used for predictive analytics highly dependent on quality of training data. This requirement demands the need for noise free data or images. The existing partial differential equation based diffusion models can remove noise present in an image but lacking in preserving thin lines, fine details and sharp corners. The classifier algorithms can able to make correct judgement to which class the image belongs to only if all edges are preserved properly during denoising process. To satisfy this requirement the authors proposed a new improved partial differential equation based diffusion algorithm for edge preserving image denoising. The proposed new anisotropic diffusion algorithm is an extension of self-snake diffusion filter which estimates edge and gradient directions as eigenvectors of a structure tensor matrix. The unique feature of this proposed anisotropic diffusion algorithm is diffusion rate at various parts of an image matches with the speed of level set flow. In the proposed algorithm an efficient edge indicator function dependent on the trace of the structure tensor matrix is used. The proposed model performs best in preserving thin lines, sharp corners and fine details since diffusion happens only along edges and diffusion is totally stopped across edges in this model. The additional edge-stopping term which is a vector dot product of derivative of an edge stopping function and derivative of an image computed along gradient and edge orthogonal directions is used in this model as shock filter which enables increased sharpness at all discontinuities. The performance of proposed diffusion algorithm is compared with other classical diffusion filters like conventional perona-malik diffusion, conventional self-snake diffusion methods.

In this paper we compare two different methods to filter prediction signals in video coding: The anisotropic diffusion filter modeled using a (modified) heat equation and the ADMM (Alternating Direction Method of Multipliers) filter using directional total variation. The two methods are based on the same idea, namely using the structures in the underlying prediction signal to control the direction of the smoothing. It is shown here, that mathematically, in a continuous setting for a certain choice of parameters, the solutions of the two problem formulations are the same.Both filters are compared in the setting of a software based on HEVC (High Efficiency Video Coding). While the results for the diffusion filter method involve the testing of four different filters of uniform and signal adaptive type, the ADMM filter results feature only one type of filter configuration that can be en- or disabled in a block-wise manner. Despite the fact that both methods rely on the same idea, it is illustrated that the overall gains of the combination are higher than the RD gains of the individual tools. In particular, it is demonstrated that this is still the case if the filters are combined in an exclusive way. This proves that there is an additional value for each of the filter methods.

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE)-based methods. The central idea of the signal adaptive filters is explained and demonstrated geometrically. The meaning of particular parameters is discussed in detail. Furthermore, thorough parameter tests are introduced which improve the overall bitrate savings. It is shown that these filters enhance the rate-distortion performance of the state-of-the-art hybrid video codecs. In particular, based on mathematical denoising techniques, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime increase of 19% and 17% decoder runtime increase. For UHD (Ultra High Definition) test sequences, bitrate savings of up to 7.36% for Random Access are accomplished.

The paper summarizes the implementation of a modified nonlinear anisotropic diffusion filter, in which local noise estimate is calculated based on mean square errror value. Moreover, unsharp masking is used for edge enhancement. This filter is implemented based on finite difference method. Experimental results on CT lungs images shows that, noise due to the artifacts is reduced and egdes are well preserved. Conventional diffusion filter works better only when brightness gradient generated by the noise is less than the edges. Edges are not preserved at course scales in scale-space filtering. The application of the new filter in CT images of lungs can help radiologists to better detect the presence of tumours and abnormal growths. The enhancement of images by the proposed method is superior to that with unsharp masking scheme employing conventional filters in terms of the visual quality, the noise performance and the computational complexity, making it an ideal candidate for latent CT images. As this algorithm contains basic operations, repeated over the image, hardware implementation can be easily done.

A simple and effective method for high-sensitivity NMR detection of selected compounds is reported. The method combines 1D NMR diffusion filter experiments and small monolayer-protected nanoparticles as high-affinity receptors. Once bound to the nanoparticles, the diffusion coefficient of the analyte decreases in such way that spectral editing based on diffusion filters can separate its signals from those of other mixture components. Using nanoparticles functionalized with Zn2+-triazacyclonane complexes, detection and identification of phosphorylated organic molecules can be achieved. Diphenyl phosphate can be detected at 25 micromolar concentration with good selectivity. The selectivity toward organic carboxylates is enhanced at pD=3.75. In these conditions, commercial tablets containing betamethasone phosphate and a large excess of benzoate could be successfully analyzed.

Optimized kernels for approximating first order spatial derivatives in the regularized Perona–Malik diffusion filter reveal an excellent degree of rotational invariance but produce small checkerboard artifacts in the processed image. In this work, our focus is to reduce these checkerboard artifacts, without a compromise in rotational invariance. To achieve the same, we propose a new 5 × 5 stencil scheme based on generalized finite difference idea. The free parameter in the proposed scheme can be set to recover various well-known schemes. Based on the numerical experiments, we suggest a value of the parameter to have less checkerboard artifacts. We also prove the consistency and the stability of the proposed scheme. A pre-smoothing with 3 × 3 Gaussian mask at every iteration step further improves the quality of the processed image. Simulation results show that the hybrid scheme obtained with such a pre-smoothing is more efficient in computational cost.

The purpose of this study was tried to remove the noise and improve the spatial resolution in the computed tomography (CT) by using anisotropic 2-dimensional (2D) diffusion based filter. We used 4-channel multi-detector CT and american association of physicists in medicine (AAPM) phantom was used for CT performance evaluation to evaluate the image quality. X-ray irradiation conditions for image acquisition was fixed at 120 kVp, 100 mAs and scanned 10 mm axis with ultra-high resolution. The improvement of anisotropic 2D diffusion filtering that we suggested firstly, increase the contrast of the image by using histogram stretching to the original image for 0.4%, and multiplying the individual pixels by 1.2 weight value, and applying the anisotropic diffusion filtering. As a result, we could distinguished five holes until 0.75 mm in the original image but, five holes until 0.40 mm in the image with improved anisotropic diffusion filter. The noise of the original image was 46.0, the noise of the image with improved anisotropic 2D diffusion filter was decreased to 33.5(27.2%). In conclusion improved anisotropic 2D diffusion filter that we proposed could remove the noise of the CT image and improve the spatial resolution.

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.

Nowadays, automatic tumor detection from brain images is extremely significant for many diagnostic as well as therapeutic purposes, due to the unpredictable shape and appearance of tumors. In medical image analysis, the automatic segmentation of tumors from brain using magnetic resonance imaging (MRI) data is the most critical issue. Existing research has some limitations, such as high processing time and lower accuracy, because of the time required for the training process. In this research, a new automatic segmentation process is introduced using machine learning and a swarm intelligence scheme. Here, a fuzzy logic with spiking neuron model (FL-SNM) is proposed for segmenting the brain tumor region in MR images. Initially, input images are preprocessed to remove Gaussian and Poisson noise using a modified Kuan filter (MKF). In the MKF, the optimal selection of the minimum MSE of image pixels is achieved using a random search algorithm (RSA), which improves the peak signal-to-noise ratio (PSNR). Then, the image is smoothed using an anisotropic diffusion filter (ADF) to reduce the over-filtering problem. Afterwards, to extract statistical texture features, Fisher’s linear-discriminant analysis (FLDA) is used. Finally, extracted features are transferred to the FL-SNM process and this scheme effectively segments the tumor region. In FL-SNM, the consequent parameters such as weight and bias play an important role in segmenting the region. Therefore, optimizing the weight parameter values using a chicken behavior-based swarm intelligence (CSI) algorithm, is proposed. The proposed (FL-SNM) scheme attained better performance in terms of high accuracy (94.87%), sensitivity (92.07%), specificity (99.34%), precision rate (89.36%), recall rate (88.39%), F-measure (95.06%), G-mean (95.63%), and DSC rate (91.2%), compared to existing convolutional neural networks (CNNs) and hierarchical self-organizing maps (HSOMs).

In this paper we combine state of the art video compression with inpainting methods. We introduce a new signal adaptive method to filter the predictions of a hybrid video codec using a constrained optimization approach. The filter is embedded into the framework of HEVC. The efficiency of the HEVC video codec is improved by up to −6.14% Bj O ntegaard delta (BD) rate for All Intra.

Advanced pictures that got by any image procedure are routinely rotted by commotion as a result of various wellsprings of blocks that impact the estimation process. A shared objective crosswise over frameworks is to build the determination however much as could reasonably be expected to accomplish genuine isotropic picture which ought to be clearer, obscure free, and less uproarious. Different diffusion-based filtering strategies have been utilized, anisotropic diffusion (AD) or nonlinear diffusion (ND), which diminishes the spot/speckle noise in medical pictures. This proposition particularly in view of speckle reduction diffusion filter (SRDF), followed by utilization of super-resolution (SR) on these sifted and fragmented medicinal pictures of various imaging modalities combined advances like filtering, determination, and improvement helps in recognizing the variation or abnormality from the norm if any present in the picture. With the assistance of machine learning (ML), one can anticipate the status of the variation from the norm precisely. Along these lines, the objective of documenting high-definition (HD) pictures from minimal effort methodology is accomplished agreeably.

牋Fingerprint enhancement and orientation field estimation are two of the core algorithms of fingerprint preprocessing. Performance of existing methods is usually poor for low-quality fingerprint images. This paper proposes to use a directional diffusion filter bank so that any local region of a fingerprint image can be always effectively enhanced by a filter of an optimal direction. A final enhanced image is obtained by selecting optimally enhanced pixels from the filter bank according to a local quality measurement based on a spectrum analysis, and at the same time, an orientation field is given by the selected filter for each pixel. Experiments show that the algorithm is superior to the existing methods and robust for images of poor quality.

The computed tomography (CT) is considered as a significant imaging tool for the clinical diagnosis. Due to the low-dose radiation in the CT, the projection data is highly affected by the Gaussian noise. Thus, there is a demand of a framework that can eliminate the noise and provide high-quality images. This paper presents a new statistical image reconstruction algorithm by proposing a suitable regularization method. The proposed framework is the combination of two basic terms, namely data fidelity and regularization. Minimizing the log-likelihood gives data fidelity term, which represents the distribution of noise in low-dose X-ray CT images. Maximum likelihood expectation maximization algorithm is introduced as a data fidelity term. The ill-posedness problem of data fidelity term is overcome with the help of complex diffusion filter. It is introduced as regularization term into the proposed framework that minimizes the noise without blurring edges and preserving the fine structure information into the reconstructed image. The proposed model has been evaluated on both simulated and real standard thorax phantoms. The final results are compared with the various other methods, and it is analyzed that the proposed model has many desirable properties such as better noise robustness, less computational cost, enhanced denoising effect.

We address the problem of inferring a directed network from nodal observations of graph signals generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs of a linear graph filter (i.e., a polynomial on a local diffusion graph-shift operator encoding the unknown graph topology), excited with an ensemble of independent graph signals with arbitrarily-correlated nodal components. In this context, we first rely on observations of the output signals along with prior statistical information on the inputs to identify the diffusion filter. Such problem entails solving a system of quadratic matrix equations, which we recast as a smooth quadratic minimization subject to Stiefel manifold constraints. Subsequent identification of the network topology given the graph filter estimate boils down to finding a sparse and structurally admissible shift that commutes with the given filter, thus forcing the latter to be a polynomial in the sought graph-shift operator. Preliminary numerical tests corroborating the effectiveness of the proposed algorithms in recovering synthetic and real-world digraphs are provided.

In this paper we combine state of the art video compression and Partial Differential Equation (PDE) based image processing methods. We introduce a new signal adaptive method to filter the predictions of a hybrid video codec using a system of PDEs describing a diffusion process. The method can be applied to intra as well as inter predictions. The filter is embedded into the framework of HEVC. The efficiency of the HEVC video codec is improved by up to −2.76% for All Intra and −3.56% for Random Access measured in Bjontegaard delta (BD) rate. Coding gains of up to −8.76% can be observed for individual test sequences.

The aim of this study is to assess the capacity of the Surface Water Ocean Topography (SWOT) satellite to resolve fine scale oceanic surface features in the western Mediterranean. Using as input the Sea Surface Height (SSH) fields from a high-resolution Ocean General Circulation Model (OGCM), the SWOT Simulator for Ocean Science generates SWOT-like outputs along a swath and the nadir following the orbit ground tracks. Given the characteristic temporal and spatial scales of fine scale features in the region, we examine temporal and spatial resolution of the SWOT outputs by comparing them with the original model data which are interpolated onto the SWOT grid. To further assess the satellite’s performance, we derive the absolute geostrophic velocity and relative vorticity. We find that instrument noise and geophysical error mask the whole signal of the pseudo-SWOT derived dynamical variables. We therefore address the impact of removal of satellite noise from the pseudo-SWOT data using a Laplacian diffusion filter, and then focus on the spatial scales that are resolved within a swath after this filtering. To investigate sensitivity to different filtering parameters, we calculate spatial spectra and root mean square errors. Our numerical experiments show that noise patterns dominate the spectral content of the pseudo-SWOT fields at wavelengths below 60 km. Application of the Laplacian diffusion filter allows recovery of the spectral signature within a swath down to the 40–60 km wavelength range. Consequently, with the help of this filter, we are able to improve the observation of fine scale oceanic features in pseudo-SWOT data, and in the estimation of associated derived variables such as velocity and vorticity.

One of the most significant areas of image research is Image Enhancement. The main aspect of image enhancement involves the improvisation of the visual manifestation of an image. Poor contrast and noise affect many kinds of images today, such as satellite images, remote sensing images, medical images, real-life images and electron microscope images. Therefore, noise removal and resolution increment are important as well as necessary to ensure and enhance the quality of images. There are many imaging modalities and each of them performs different functions ranging from the provision of information about human anatomy/structure to the provision of location statistics about specific activities and tasks. Physical constraints of system detectors—which are tuned to signal-to-noise and timing considerations are used to determine the resolution of imaging systems. The hybrid techniques designed n this paper uses algorithms are mostly based on standard diffusion filters and SR algorithms. Results demonstrate the potential in introducing SR techniques into practical medical applications.

Asymmetry analysis of thermal images plays a prominent role in automated diagnosis of normal and abnormal breast tissues. Accurate segmentation of breast tissues further enhances the analysis. In this work, asymmetry analysis of breast thermal images is carried out using efficient fractal algorithm. Prior to feature extraction, the breast tissues are delineated by integrating anisotropy diffusion filter into modified level set framework. In order to validate the segmented results against ground truth images, Overlap measures are estimated. Breast regions such as left and right are separated from the segmented tissues. A set of binary images are generated by subjecting the separated breasts to thresholded binary decomposition (TBD) method. The features such as fractal dimensions, mean gray level and pixel count are computed from the resultant binary images. The segmented results had shown high degree of correlation against the ground truth images. Accuracy of the segmented results is found to be 98%. Asymmetry analysis using TBD method shows that the features are capable of discriminating the change in structural information which is caused by varied metabolic conditions. Finally, mean gray level feature show 11% improvement in demarcating normal and abnormal breast tissues. Therefore, the proposed method appears to be efficient in computer diagnosis of early breast abnormalities using infrared images.

Standard second order speckle reducing diffusion filters produce staircase artefacts (step edges) in the filtered image. In this paper we present an anisotropic fourth order diffusion filter with diffusion coeficients as functions of speckle statistics.This filter steers the diffusion unevenly in the directions of gradient and level set, and hence preserves the edges while removing the noise. The experiments show that the proposed filter outperforms the existing second order speckle reducing filters in terms of staircase artefacts without destructing the edges.