Anisotropic Gaussian Filter Research Articles

Direct segmentation of cortical cytoarchitectonic domains using ultra-high-resolution whole-brain diffusion MRI.

We assess the potential of detecting cortical laminar patterns and areal borders by directly clustering voxel values of microstructural parameters derived from high-resolution mean apparent propagator (MAP) magnetic resonance imaging (MRI), as an alternative to conventional template-warping-based cortical parcellation methods. We acquired MAP-MRI data with 200 µ m resolution in a fixed macaque monkey brain. To improve the sensitivity to cortical layers, we processed the data with a local anisotropic Gaussian filter determined voxel-wise by the plane tangent to the cortical surface. We directly clustered all cortical voxels using only the MAP-derived microstructural imaging biomarkers, with no information regarding their relative spatial location or dominant diffusion orientations. MAP-based 3D cytoarchitectonic segmentation revealed laminar patterns similar to those observed in the corresponding histological images. Moreover, transition regions between these laminar patterns agreed more accurately with histology than the borders between cortical areas estimated using conventional atlas/template-warping cortical parcellation. By cross-tabulating all cortical labels in the atlas- and MAP-based segmentations, we automatically matched the corresponding MAP-derived clusters (i.e., cytoarchitectonic domains) across the left and right hemispheres. Our results demonstrate that high-resolution MAP-MRI biomarkers can effectively delineate three-dimensional cortical cytoarchitectonic domains in single individuals. Their intrinsic tissue microstructural contrasts enable the construction of whole-brain mesoscopic cortical atlases.

An edge detection algorithm based upon the adaptive multi-directional anisotropic gaussian filter and its applications

An edge detection algorithm based upon the adaptive multi-directional anisotropic gaussian filter and its applications

Analytical Design of Gaussian Anisotropic 2D FIR Filters and Their Implementation Using the Block Filtering Approach

This work proposes an analytical design procedure for a particular class of 2D filters, namely anisotropic Gaussian FIR filters. The design is achieved in the frequency domain and starts from a low-pass Gaussian 1D prototype with imposed specifications, whose frequency response is efficiently approximated by a factored trigonometric polynomial using the Chebyshev series. Then, using specific 1D to 2D frequency mappings applied to the prototype, the frequency response for a 2D anisotropic filter with a specified orientation angle is directly derived in two versions, namely with a straight or elliptical shape in the frequency plane. The resulting filters have an accurate shape with low distortion. Several design examples for specified parameters (angle and selectivity) are provided. Then, simulations of directional filtering on various test images are given, which show their capability of extracting oriented lines or other various oriented objects from synthetic or real-life images. Finally, a computationally efficient implementation at the system level is proposed, based on a polyphase decomposition and block-filtering approach, which yields a 2D filter with a high degree of parallelism and low arithmetic complexity.

Improved Anisotropic Gaussian Filters

Elongated anisotropic Gaussian filters are used for the orientation estimation of fibers. In cases where computed tomography images are noisy, roughly resolved, and of low contrast, they are the method of choice even if being efficient only in virtual 2D slices. However, minor inaccuracies in the anisotropic Gaussian filters can carry over to the orientation estimation. Therefore, this paper proposes a modified algorithm for 2D anisotropic Gaussian filters and shows that this improves their precision. Applied to synthetic images of fiber bundles, it is more accurate and robust to noise. Finally, the effectiveness of the approach is shown by applying it to real-world images of sheet molding compounds.

An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images

Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation.

A novel dual-fusion algorithm of single image dehazing based on anisotropic diffusion and Gaussian filter

Dark channel prior (DCP) is a widely used method in single image dehazing technology. Here, we propose a novel dual-fusion algorithm of single image dehazing based on anisotropic diffusion and Gaussian filter to suppress the halo effect or colour distortion in traditional DCP algorithms. Anisotropic diffusion is used to edge-preserving smooth images and a Gaussian filter is to smooth the local white objects. A dual-fusion strategy is conducted to optimise the atmospheric veil. Besides, the fast explicit diffusion (FED) scheme is used to accelerate the numerical solution of the anisotropic diffusion to reduce time consumption. The subjective and objective evaluation of the experiment shows that the proposed algorithm can effectively suppress the halo effect and colour distortion, and has good dehazing performance on evaluation metrics. The proposed algorithm also reduces the time consumption by 54.2% than DCP with guided filter. This study provides an effective solution for single image dehazing.

A novel dual-fusion algorithm of single image dehazing based on anisotropic diffusion and Gaussian filter

A novel dual-fusion algorithm of single image dehazing based on anisotropic diffusion and Gaussian filter

Guided Filtering Combining Neighborhood Variance and Anisotropic Window

Aiming at the problem that guided filtering would cause halo near the edge and it is difficult to identify fine edges, a guided filtering combining neighborhood variance and anisotropic window is proposed. Firstly, the directional selectivity of the anisotropic Gaussian filter is used for the recognition of the fine edge, and the narrow spatial structure of the filter can benefit the information fusion of different pixels inside the local window to suppress the edge blur and halo effect. Secondly, based on the local structure similarity, the neighborhood variance is introduced to optimize the parameters of local linear transformation, so as to achieve the maximum diffusion at non-edge region while preserving strong edges. The experimental results on 102 Category Flower Dataset show that, compared with other edge-preserving filtering methods, the proposed method is superior to other methods in visual effect and quantitative evaluation (PSNR and SSIM), and the distortion of the test image is 46.72%, 48.64% and 29.61% smaller than guided filtering, weighted guided filtering and anisotropic guided filtering respectively. It can effectively suppress the occurrence of artifacts while recognizing precise edges.

Carotid Ultrasound Boundary Study (CUBS): Technical considerations on an open multi-center analysis of computerized measurement systems for intima-media thickness measurement on common carotid artery longitudinal B-mode ultrasound scans

After publishing an in-depth study that analyzed the ability of computerized methods to assist or replace human experts in obtaining carotid intima-media thickness (CIMT) measurements leading to correct therapeutic decisions, here the same consortium joined to present technical outlooks on computerized CIMT measurement systems and provide considerations for the community regarding the development and comparison of these methods, including considerations to encourage the standardization of computerized CIMT measurements and results presentation. A multi-center database of 500 images was collected, upon which three manual segmentations and seven computerized methods were employed to measure the CIMT, including traditional methods based on dynamic programming, deformable models, the first order absolute moment, anisotropic Gaussian derivative filters and deep learning-based image processing approaches based on U-Net convolutional neural networks. An inter- and intra-analyst variability analysis was conducted and segmentation results were analyzed by dividing the database based on carotid morphology, image signal-to-noise ratio, and research center. The computerized methods obtained CIMT absolute bias results that were comparable with studies in literature and they generally were similar and often better than the observed inter- and intra-analyst variability. Several computerized methods showed promising segmentation results, including one deep learning method (CIMT absolute bias = 106 ± 89 μm vs. 160 ± 140 μm intra-analyst variability) and three other traditional image processing methods (CIMT absolute bias = 139 ± 119 μm, 143 ± 118 μm and 139 ± 136 μm). The entire database used has been made publicly available for the community to facilitate future studies and to encourage an open comparison and technical analysis (https://doi.org/10.17632/m7ndn58sv6.1).

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries. The scanned invoice image is often noisy due to various reasons. This affects the OCR (optical character recognition) detection accuracy. In this paper, letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method. A stacked denoising autoencoder (SDAE) is implemented with two hidden layers each in encoder network and decoder network. In order to capture the most salient features of training samples, a undercomplete autoencoder is designed with non-linear encoder and decoder function. This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy. A dataset consisting of 59,119 letter images, which contains both English alphabets (upper and lower case) and numbers (0 to 9) is prepared from many scanned invoices images and windows true type (.ttf) files, are used for training the neural network. Performance is analyzed in terms of Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Universal Image Quality Index (UQI) and compared with other filtering techniques like Nonlocal Means filter, Anisotropic diffusion filter, Gaussian filters and Mean filters. Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values. Results show the superior performance of proposed SDAE method.

LATENT FINGERPRINT IMAGE ENHANCEMENT BASED ON OPTIMIZED BENT IDENTITY BASED CONVOLUTIONAL NEURAL NETWORK

Fingerprints are unique biometric systems (BSs) in which none of the human possesses similar fingerprint structures. It is one of the most significant biometric processes used in the identification of criminals. Latent fingerprints or latents are generated mainly by the finger sweat or oil deposits which is left by the suspects unintentionally. The impressions of latents are blurred or smudgy in nature and not viewed by naked eye. These fingerprints are of low quality, corrupted by noise, degraded by technological factors and exhibit minor details. Latents display consistent structural info when observed as an image. Image Enhancement is necessary in latents, to transform the latent (noisy) image into fine-quality (enhanced) image. In this work, a new image enhancement approach named BI-CNN (Bent Identity-Convolution Neural Network) with Spatial Pyramid Max Pooling (SPMP) model optimized using TSOA (Tunicate Swarm Optimization Algorithm) is presented to produce an enhanced latent at the output. This procedure involves the integration of ROI (Region Of Interest) Estimation, Anisotropic Gaussian Filter (AGF) based Pre-filtering, Fingerprint alignment using Sobel Filter, Intrinsic Feature patch extraction using Optimized BI-CNN, GAT (Graph Attention) network based Similarity Estimation followed by image reconstruction and feedback module. The implementation tool used in this work is PYTHON platform. The proposed optimized BI-CNN framework tested on dual public datasets namely IIITD-latent finger print and IIITD-MOLF have shown enhanced outcomes. Thus, the IIITD -latent fingerprint database obtained 83.33% on Rank-10 accuracy and 39.33% on Rank-25 accuracy.

New Method for Characterizing Internal Structure of Fault-Karst Reservoirs and Analysis on Acidizing Fracturing Effect: A Case Study in HLHT Oilfield, Tarim Basin, NW China

AbstractSuperimposed tectonic movement and karst erosion resulted in a combination of fractures and irregular caves in deep/ultradeep carbonate rocks, typically along major fault swarms. Outlining these fault-karst reservoirs mainly depends on recognizing the strong reflection in seismic profiles; however, characterizing their internal structures is still difficult, which are represented as weak amplitude in seismic profiles. This study intended to propose a method to dissect the internal structure of fault-karst reservoirs, which contains four steps: (1) elimination of the signal interference by the covering bed with strong energy and recognition of internal reservoirs with low energy based on seismic data conversion, frequency division, and inversion; (2) gradient structure tensor analysis based on an anisotropic Gaussian filter for fault-karst reservoir outlining; (3) internal faults and cave recognition on the basis of wave-based inversion; and (4) reevaluation of the number and scale of these faults and caves based on seismic recognition and well test results and verification of their volumes and hydrocarbon reserves. The method was used in the evaluation of the fault-karst reservoir in the Halahatang (HLHT) oilfield, which is located in the north of Tarim Basin. The results show that the fault-karst reservoirs along major faults and their internal structures are effectively recognized, and the error of the predicted depth of the reservoirs decreases from more than 20 m before to less than 4 m now; the drilling success ratio increases from 70% to 90%. In addition, the method supports the recognition of untapped fault-karst reservoirs around shut-in wells, which provides guidance for sidetracking plans. Further, by comparing the geophysical volume of fault-karst reservoirs and the reserve predicted by production performance, the untapped reserve in a certain reservoir can be evaluated; on this basis, producing wells received high yields by targeted acid fracturing. In summary, the method effectively improves the prediction accuracy and the recovery efficiency of fault-karst reservoirs.

Corner Detection Using Second-Order Generalized Gaussian Directional Derivative Representations.

Corner detection is a critical component of many image analysis and image understanding tasks, such as object recognition and image matching. Our research indicates that existing corner detection algorithms cannot properly depict the difference between edges and corners and this results in wrong corner detections. In this paper, the capability of second-order generalized (isotropic and anisotropic) Gaussian directional derivative filters to suppress Gaussian noise is evaluated. The second-order generalized Gaussian directional derivative representations of step edge, L-type corner, Y- or T-type corner, X-type corner, and star-type corner are investigated and obtained. A number of properties for edges and corners are discovered which enable us to propose a new image corner detection method. Finally, the criteria on detection accuracy and average repeatability under affine image transformation, JPEG compression, and noise degradation, and the criteria on region repeatability are used to evaluate the proposed detector against nine state-of-the-art methods. The experimental results show that our proposed detector outperforms all the other tested detectors.

Learning-Free Text Line Segmentation for Historical Handwritten Documents

We present a learning-free method for text line segmentation of historical handwritten document images. This method relies on automatic scale selection together with second derivative of anisotropic Gaussian filters to detect the blob lines that strike through the text lines. Detected blob lines guide an energy minimization procedure to extract the text lines. Historical handwritten documents contain noise, heterogeneous text line heights, skews and touching characters among text lines. Automatic scale selection allows for automatic adaption to the heterogeneous nature of handwritten text lines in case the character height range is correctly estimated. In the extraction phase, the method can accurately split the touching characters among the text lines. We provide results investigating various settings and compare the model with recent learning-free and learning-based methods on the cBAD competition dataset.

Corner Detection Using Multi-directional Structure Tensor with Multiple Scales

Corners are important features for image analysis and computer vision tasks. Local structure tensors with multiple scales are widely used in intensity-based corner detectors. In this paper, the properties of intensity variations of a step edge, L-type corner, Y- or T-type corner, X-type corner, and star-type corner are investigated. The properties that we obtained indicate that the image intensity variations of a corner are not always large in all directions. The properties also demonstrate that existing structure tensor-based corner detection methods cannot depict the differences of intensity variations well between edges and corners which result in wrong corner detections. We present a new technique to extract the intensity variations from input images using anisotropic Gaussian directional derivative filters with multiple scales. We prove that the new extraction technique on image intensity variation has the ability to accurately depict the characteristics of edges and corners in the continuous domain. Furthermore, the properties of the intensity variations of step edges and corners enable us to derive a new multi-directional structure tensor with multiple scales, which has the ability to depict the intensity variation differences well between edges and corners in the discrete domain. The eigenvalues of the multi-directional structure tensor with multiple scales are used to develop a new corner detection method. Finally, the criteria on average repeatability (under affine image transformation, JPEG compression, and noise degradation), region repeatability based on the Oxford dataset, repeatability metric based on the DTU dataset, detection accuracy, and localization accuracy are used to evaluate the proposed detector against ten state-of-the-art methods. The experimental results show that our proposed detector outperforms all the other tested detectors.

Research on image de‐disturbing algorithm based on dark channel prior and anisotropic Gaussian filtering

SummaryIn order to solve the problem of serious degradation of images collected outdoors in dense fog weather, a defogging algorithm for dense fog images was proposed. The fog‐day imaging physical model was simplified; the concept of fog concentration factor was proposed. The single image de‐hazing algorithm based on dark channel priors solves the problem of estimating the transmittance of fog and sky scenes. The image recovered by the algorithm is clear and natural. The algorithm has high computational complexity. It takes a long time to meet real‐time requirements. The principle of anisotropic Gaussian filtering is introduced. It combines with the dark channel. The value of the fog concentration coefficient is obtained by estimating the visibility value of a single fog image. It is then combined with an anisotropic Gaussian filter of the image to estimate the atmospheric light value. Defogging of the fog image is performed by using the repair function. It is necessary to perform effective de‐hazing processing on smog images. Experiments show that the improved algorithm can greatly reduce the complexity of the algorithm while ensuring the defogging effect of the original algorithm.

Detection algorithm for turbulent interfaces and large-scale structures in intermittent flows

A robust algorithm is introduced for detection of large-scale coherent structures in transitional and intermittent flows that feature turbulent/non-turbulent (T/NT) interfaces. The algorithm is applicable the instantaneous flow fields of wall-bounded and free shear flows, and can effectively identify coherent events in the velocity or vorticity fields, or sweep/ejection motions. A database from direct numerical simulation (DNS) of transitional boundary layer is used to develop and demonstrate the capabilities of the algorithm which consists of three steps. The first is identification of the T/NT interface by comparing the normalized vorticity magnitude to a threshold value that is independent of the Reynolds number. The vorticity normalization is specifically designed to be applicable in transitional flows, where regions of the flow can host juxtaposed regions of laminar and turbulent flow. With the definition of the T/NT interface, conditional statistics are computed and perturbation quantities are defined relative to their respective conditional means. Second, the influence of the small-scale turbulence is excluded by applying an anisotropic Gaussian filter. The filter size is determined from the spatial characteristics of the small-scale vortical motions. In the third step, one-dimensional cores and two-dimensional surfaces within the flow structures of interest are identified from local extrema in the fields, and are tracked as Lagrangian objects. Using the algorithm, the population trends and advection speeds of large-scale sweep/ejection events are computed in the transitional boundary layer. Two additional flow configurations are also considered: turbulent jet flow emerging from a circular nozzle and the turbulent flow in a channel with a wavy surface.

A Gaussian filtering method to reduce directionality on high-density point clouds digitized by a conoscopic holography sensor

This work analyses the directional effect shown by the high-density point clouds digitized with a conoscopic holography (CH) sensor. The asymmetric shape of the laser spot for this sensor yields directionality to appear along the largest spot dimension and to occur repeatedly under different working conditions. To study this effect, several digitizing tests were performed under different conditions on a surface machined by EDM with a uniform and isotropic finish, so that the directional effect should not appear. Nevertheless, the use of the 2D Fourier transform (2DFT) confirmed the existence of directionality in the point clouds along the largest spot direction and that it appeared repetitively under different working conditions. Thus, this effect could be considered as a systematic error associated to the CH sensor and then, feasible to be reduced. The use of an anisotropic 2D Gaussian filter is suggested for this purpose. The results found before and after applying the filter were compared to those obtained by a confocal microscope, which was used as reference due to the absence of directionality in the captured images. Results show that the filtered point clouds suitably fit the actual surface topography.

Comparison and error estimation of 3D fibre orientation analysis of computed tomography image data for fibre reinforced composites

Fibre orientation represents one of the most important micro-structural characteristics for fibre reinforced composite materials. Its influence on mechanical behaviour, material anisotropy and damage evolution has, especially with the modern trend to mass production of structural composite parts, moved back into the focus of micro-structural analysis. Following this development and the enormous improvements of X-ray computed tomography resolution and evaluation techniques in the field of composites the 3D analysis of fibre orientations throughout macroscopic components has become possible. Therefore, the complex connection between manufacturing process and manifested fibre architecture can be determined and correlated. Various image analysis methods exist in the material science and image analysis community which can be employed to extract fibre orientations of an image. In this work we compare three methods based on anisotropic Gaussian filtering, Hessian matrix calculation or structure tensor calculation in their ability to describe correct fibre orientations and their error-proneness with respect to imaging modalities and material composition. Those methods have become popular also in commercial software platforms for micro-structure analysis in user-friendly forms. All three algorithms were implemented with the open source imaging tool-kit ITK. In order to compare those methods, error estimates and testing procedures will be described herein.

A robust anisotropic edge detection method for carotid ultrasound image processing

A new approach for robust edge detection on B-mode ultrasound images of the carotid artery is proposed in this paper. The proposed method uses anisotropic Gaussian derivative filters along with non-maximum suppression over the overall artery wall orientation in local regions. The anisotropic filters allow using a wider integration scale along the edges while preserving the edge location precision. They also perform edge continuation, resulting in the connection of isolated edge points along linear segments, which is a valuable feature for the segmentation of the artery wall layers. However, this usually results in false edges being detected near convex contours and isolated points. The use of non-maximum suppression over pooled local orientations is proposed to solve this issue. Experimental results are provided to demonstrate that the proposed edge detector outperforms other common methods in the detection of the lumen-intima and media-adventia layer interfaces of the carotid vessel walls. Additionally, the resulting edges are more continuous and precisely located.