Bilateral Filter Research Articles

Bilateral Half-Box Image Filtering

Because edge-aware filtering has been widely used for image-based automation technologies, developing a practical, fast algorithm for its use is important. This letter proposes a simple and fast computational method for edge-aware image filtering based on bilateral half-box regions. Our filter consists of a weighted average of only eight color sums within each half-box region adjacent to a given pixel, where the tonal weights are similar to those obtained with bilateral filtering. The eight sums are efficiently obtained by a single fast box filter using the relative coordinate relationship between the pixel and each half-box center. We examined the performance of our filter based on comparisons with conventional methods.

Abstract 1072: Two Filter, Or Not Two Filter: Case Report Of Rare Anatomical Variant Of Inferior Vena Cava

This case involves bilateral inferior vena caval filter placement in a 68-year-old male with a history of a recently diagnosed pulmonary embolism (PE) who presented with precipitously dropping hemoglobin and dark tarry stools after starting Eliquis. Endoscopy confirmed ulcerative esophagitis. Interventional radiology was consulted for inferior vena cava (IVC) filter placement as was indicated due to lower extremity thrombosis, PE, and GI bleed. During the procedure, cavography revealed a duplicated inferior vena cava (DIVC), prompting the placement of bilateral Denali filters infrarenally via the right and left common femoral approach. Only 16 similar cases are reported in the literature at the time of this literature review, and this case highlights the efficacy of bilateral infrarenal filter placement in addressing this anatomical variation.

Skin cancer classification enabled mobile neuro fuzzy network and entropy with weber local binary pattern based for feature extraction

Skin cancer (SC) is the most quickly spreading diagnosis all over the world due to limited resources presented. Accurately diagnosing skin lesions in their early stages could help clinicians make better decisions by raising the likelihood of a cure before cancer develops. However, because to the imbalance and scarcity of most skin illness images used for training, it is challenging to achieve automatic skin cancer classification. Also, it experiences difficulties like high consumption of time. Deep learning (DL) modules are introduced to deal with these difficulties as well as it aids dermatologists in creating precise examinations. To conquer this concern, a novel entropy system for SC classification is developed by a hybrid network named as Mobile Neuro Fuzzy Network (MNFN), where MNFN is the fusion of MobileNet and Cascade Neuro fuzzy Network (cascade NFN). The input image is forwarded to the pre-processing unit, which is done by using a bilateral filter. Skin lesion segmentation is done by Squeeze U-SegNet. Feature extraction is performed using the proposed Weber Local Binary Pattern (WLBP) with entropy is also extracted. Then, data augmentation (DA) by oversampling scheme Synthetic Minority Oversampling Technique (SMOTE) is performed. Finally, SC classification is executed by employing a hybrid model MNFN. Here the SC is classified into various class labels. The analytic metrics used for MNFN namely, accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate(FPR) and False Negative Rate (FNR) gained suitable values of 0.930, 0.945, 0.931, 0.110 and 0.094.

Denoising multispectral images using non-local rank tensor decomposition and bilateral filtering based on sunflower optimization

ABSTRACT Image denoising is an important pre-processing process in the fields of computer vision and image processing. Traditional denoising techniques blur edges excessively and degrade image quality by removing noise components but failing to maintain clarity. To overcome these problems, this paper proposes a multispectral image denoising strategy combining non-local rank tensor decomposition (NLRTD) and bilateral filtering. To extract patches from noisy images, single-level discrete wavelet transform (DWT) is utilized. Then, similar patches from the extracted images are grouped using spectral clustering. After that, mixed noise is reduced by separating clean images from each clustered group using NLRTD. An optimized bilateral filter using Sunflower optimization (SFO) is used for denoising by preserving edge details and is reconstructed using its constituent parts. The effectiveness of the proposed denoising method is assessed using performance matrices, such as BER, PSNR, MSE, RMSE, SNR and SSIM were 0.8544%, 53.21%, 2.41%, 2.41%, 25.06% and 0.90%, respectively.

Bilateral regularized optimization model for edge-preserving image smoothing

Edge-preserving image smoothing is vital in the field of image processing and computational photography. The state-of-the-art filters based on optimization models have achieved promising performance. However, most of them fail to consider the spatial support in the regularization term, thus limiting the edge-preserving capabilities. In this paper, inspired by the bilateral filter, which consists of a range kernel and a spatial kernel. we propose to leverage bilateral kernel as a penalty function, and embed it into an optimization model for edge-preserving image smoothing. Furthermore, we propose to incorporate an edge-aware weighted scheme in the data term design, which further improves the edge-preserving capability. The bilateral function is non-convex and can be non-trivial to solve. In this paper, we propose a novel iterative solution based on fixed point iteration, where the main burden in each iteration is a bilateral filtering process. We have conducted extensive experiments to evaluate the proposed filter. Experiment results indicate that our filter benefits a variety of image processing tasks. Moreover, we propose an efficient approximation of the proposed filter, which is able to significantly accelerate the filtering process with neglectable sacrifice of smoothing quality.

Automated, quick, and precise building extraction from aerial images using ll-unet model

The subject matter of this article is the detection and semantic segmentation of buildings from high-resolution aerial images. It extracts building images from similar characteristics of roads and soil objects. It is used for various applications such as urban planning, infrastructure development, and disaster management. The goal of this study is to develop a fast, accurate, and automatic building detection model based on the semantic segmentation LL-UNET architecture that is optimized and tuned with proper hyper parameter settings. The tasks to be solved are as follows: collect remote sensing building dataset that is divided into three parts of training, validation, and testing; apply data augmentation on the training dataset by vertical flip, horizontal flip, and rotation methods; further pass into the bilateral filter to remove noises from the images; optimize LL-UNET model by various optimizer methods and tuned hyper parameter by proper selection value, the method is compared by the performance metrics recall, precision, and accuracy. The following results were obtained: the model is evaluated under the training loss curve and accuracy curve of different optimizers SGD, ASGD, ADAM, ADAMW, and RMSProp; it measures the training time, mean accuracy, and mean IOU parameters during the training phase; the testing phase is evaluated by precision and recall; the method is compared by visualizing the result of LL-UNET + different optimizers; and the proposed method is compared with the existing method by common evolution parameters metric. Conclusions. The scientific novelty of the results obtained is as follows: 1) the LL-UNET effectively segmented the building remotely sensed images in the limited number of training samples available; 2) the loss function of the model observed under hyper parameter selection of the optimizer, learning rate, batch size, and epochs; which makes an optimal model to extract the building in an accurate and fast manner from the complex background; 3) the proposed model results compared with a well-known model of the building extraction under the common evaluation metrics of F1 score.

A Comparative Study on X-Ray image Enhancement Based on Neutrosophic Set

Medical image noise, ambiguity, and fuzziness pose challenges to the medical image analysis process. To lessen these problems, fuzzy sets are utilized; however, they frequently ignore the pixel's spatial context. The neutrosophic set (NS) is employed in picture denoising to get over these restrictions. Neutronosophic theory, in particular the NS Bilateral filter, NS Wiener filter, NS Median filter, NS gaussian filter, and NS rank-ordered filter, is covered in this paper. The Lung-cancer dataset of X-Ray images is used to assess the performance of different denoising techniques. The effectiveness of NS-based denoising techniques over conventional techniques is demonstrated through comparisons with other approaches, demonstrating the NS's function in X-ray image denoising.

Generalized and robust model for GAN-generated image detection

Generative adversarial network (GAN)-generated image detection is a crucial yet challenging problem. The advancement of diverse generative models makes it simpler to build realistic synthetic images, but it also increases the likelihood of using images maliciously. Recent techniques of fake image detection often overfit to specific GANs, incapable of generalizing to other GANs. Each GAN leaves its distinct fingerprint on the images it generates. We propose a simple and compact attention network that exploits high-frequency and noise residual patterns of images. We design two-channel image representations in the preprocessing phase, with one channel using a bilateral high pass filter (BiHPF) for high-frequency patterns and the other using photo response non-uniformity (PRNU) for noise residuals capturing from GAN-generated and real images. The feature maps pass through the attention network with a dense layer for classification tasks. We assess the generalizability and robustness of the model through extensive experiments. The model is evaluated on a benchmark dataset in various settings, including cross-category, cross-GAN models with varying class settings and different color manipulations. This study demonstrates that the proposed model outperforms state-of-the-art (SOTA) methods by approximately 5% in different experiment settings.

Approximate bilateral filters for real-time and low-energy imaging applications on FPGAs

Bilateral filtering is an image processing technique commonly adopted as intermediate step of several computer vision tasks. Opposite to the conventional image filtering, which is based on convolving the input pixels with a static kernel, the bilateral filtering computes its weights on the fly according to the current pixel values and some tuning parameters. Such additional elaborations involve nonlinear weighted averaging operations, which make difficult the deployment of bilateral filtering within existing vision technologies based on real-time and low-energy hardware architectures. This paper presents a new approximation strategy that aims to improve the energy efficiency of circuits implementing the bilateral filtering function, while preserving their real-time performances and elaboration accuracy. In contrast to the state-of-the-art, the proposed technique allows the filtering action to be on the fly adapted to both the current pixel values and to the tuning parameters, thus avoiding any architectural modification or tables update. When hardware implemented within the Xilinx Zynq XC7Z020 FPGA device, a 5 × 5 filter based on the proposed method processes 237.6 Mega pixels per second and consumes just 0.92 nJ per pixel, thus improving the energy efficiency by up to 2.8 times over the competitors. The impact of the proposed approximation on three different imaging applications has been also evaluated. Experiments demonstrate reasonable accuracy penalties over the accurate counterparts.

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Automated Fabric Defect (FD) detection plays a crucial role in industrial automation within fabric production. Traditionally, the identification of FDs heavily relies on manual assessment, facilitating prompt repairs of minor defects. However, the efficiency of manual recognition diminishes significantly as labor working hours increase. Consequently, there is a pressing need to introduce an automated analysis method for FD recognition to reduce labor costs, minimize errors, and improve fabric quality. Many researchers have devised defect detection systems utilizing Machine Learning (ML) approaches, enabling swift, accurate, and efficient identification of defects. This study presents the Optimal Artificial Neural Network-based Fabric Defect Detection and Classification (OANN-FDDC) technique. The OANN-FDDC technique exploits handcrafted features with a parameter-tuning strategy for effectively detecting the FD process. To obtain this, the OANN-FDDC technique employs CLAHE and Bilateral Filtering (BF) model-based contrast augmentation and noise removal. Besides, the OANN-FDDC technique extracts shape, texture, and color features. For FD detection, the ANN method is utilized. To improve the detection results of the ANN method, the Root Mean Square Propagation (RMSProp) optimization technique is used for the parameter selection process. The simulation outputs of the OANN-FDDC technique were examined on an open fabric image database. The experimental results of the OANN-FDDC technique implied a better outcome than the 96.97% of other recent approaches.

Arbitrary-Scale Video Super-resolution Guided by Dynamic Context

We propose a Dynamic Context-Guided Upsampling (DCGU) module for video super-resolution (VSR) that leverages temporal context guidance to achieve efficient and effective arbitrary-scale VSR. While most VSR research focuses on backbone design, the importance of the upsampling part is often overlooked. Existing methods rely on pixelshuffle-based upsampling, which has limited capabilities in handling arbitrary upsampling scales. Recent attempts to replace pixelshuffle-based modules with implicit neural function-based and filter-based approaches suffer from slow inference speeds and limited representation capacity, respectively. To overcome these limitations, our DCGU module predicts non-local sampling locations and content-dependent filter weights, enabling efficient and effective arbitrary-scale VSR. Our proposed multi-granularity location search module efficiently identifies non-local sampling locations across the entire low-resolution grid, and the temporal bilateral filter modulation module integrates content information with the filter weight to enhance textual details. Extensive experiments demonstrate the superiority of our method in terms of performance and speed on arbitrary-scale VSR.

Research on Underwater Image Enhancement Algorithm

In recent years, with the continuous expansion of human activity areas, the Marine pollution caused by human activities has become increasingly serious, and the large influx of agricultural emissions and household garbage needs to rely on underwater equipment to clean up. However, to identify garbage from the complex underwater environment, it is necessary to study the underwater image enhancement algorithm. Therefore, this paper takes underwater plastic floaters as the research object, starts with the optical imaging model of underwater images, analyzes the process and mechanism of underwater optical imaging, and its impact on image quality, so as to better understand the causes of image degradation. Based on white balance algorithm, histogram equalization algorithm, bilateral filter algorithm and gamma correction method, the underwater image recognition image is improved. The principle of these methods to enhance underwater image is analyzed and applied to further improve the effect of underwater image enhancement.

An adaptive anisotropic bilateral filtering method for mesh data in scale space

Three-dimensional mesh data of parts, such as blades and engine bodies, have been widely used in industrial fields. Due to the different kinds of noise during mesh acquisition and the machining deficiency of parts, the mesh quality tends to be insufficient for subsequent operations. Therefore, mesh denoising is a necessary and critical procedure to improve mesh quality. Existing methods commonly apply geometry features smoothing, which may also create unexpected results, such as volume shrinkage and blurring of sharp edges. This paper proposed an adaptive anisotropic bilateral filtering method for mesh data in scale space. Firstly, the mesh is decomposed into a smooth base with low frequency and a height vector field with high frequency based on scale space theory. The denoising of the vertex spatial field is transformed into the denoising of the height vector field, aiming to only consider high-frequency information. Secondly, the bilateral filter scheme with the anisotropic Gaussian kernel is proposed to denoise the height vector field, removing noise mixed with features. The parameters in the bilateral filter scheme are chosen adaptively by maximizing the designed probability density function. The mean square angular error of the proposed method is less than 0.15 rad, which is superior to the general-purpose algorithms, for instance, Laplacian filtering, bilateral mesh filtering and bilateral normal filtering algorithm.

An efficient breast cancer classification model using bilateral filtering and fuzzy convolutional neural network

BC (Breast cancer) is the second most common reason for women to die from cancer. Recent workintroduced a model for BC classifications where input breast images were pre-processed using median filters for reducing noises. Weighed KMC (K-Means clustering) is used to segment the ROI (Region of Interest) after the input image has been cleaned of noise. Block-based CDF (Centre Distance Function) and CDTM (Diagonal Texture Matrix)-based texture and shape descriptors are utilized for feature extraction. The collected features are reduced in counts using KPCA (Kernel Principal Component Analysis). The appropriate feature selection is computed using ICSO (Improved Cuckoo Search Optimization). The MRNN ((Modified Recurrent Neural Network)) values are then improved through optimization before being utilized to divide British Columbia into benign and malignant types. However, ICSO has many disadvantages, such as slow search speed and low convergence accuracy and training an MRNN is a completely tough task. To avoid those problems in this work preprocessing is done by bilateral filtering to remove the noise from the input image. Bilateral filter using linear Gaussian for smoothing. Contrast stretching is applied to improve the image quality. ROI segmentation is calculated based on MFCM (modified fuzzy C means) clustering. CDTM-based, CDF-based color histogram and shape description methods are applied for feature extraction. It summarizes two important pieces of information about an object such as the colors present in the image, and the relative proportion of each color in the given image. After the features are extracted, KPCA is used to reduce the size. Feature selection was performed using MCSO (Mutational Chicken Flock Optimization). Finally, BC detection and classification were performed using FCNN (Fuzzy Convolutional Neural Network) and its parameters were optimized using MCSO. The proposed model is evaluated for accuracy, recall, f-measure and accuracy. This work’s experimental results achieve high values of accuracy when compared to other existing models.

Expanding Sparse Radar Depth Based on Joint Bilateral Filter for Radar-Guided Monocular Depth Estimation.

Radar data can provide additional depth information for monocular depth estimation. It provides a cost-effective solution and is robust in various weather conditions, particularly when compared with lidar. Given the sparse and limited vertical field of view of radar signals, existing methods employ either a vertical extension of radar points or the training of a preprocessing neural network to extend sparse radar points under lidar supervision. In this work, we present a novel radar expansion technique inspired by the joint bilateral filter, tailored for radar-guided monocular depth estimation. Our approach is motivated by the synergy of spatial and range kernels within the joint bilateral filter. Unlike traditional methods that assign a weighted average of nearby pixels to the current pixel, we expand sparse radar points by calculating a confidence score based on the values of spatial and range kernels. Additionally, we propose the use of a range-aware window size for radar expansion instead of a fixed window size in the image plane. Our proposed method effectively increases the number of radar points from an average of 39 points in a raw radar frame to an average of 100 K points. Notably, the expanded radar exhibits fewer intrinsic errors when compared with raw radar and previous methodologies. To validate our approach, we assess our proposed depth estimation model on the nuScenes dataset. Comparative evaluations with existing radar-guided depth estimation models demonstrate its state-of-the-art performance.

Hyperspectral Image Classification Using Improved Active Deep Learning Framework Including Edge Preserving Filters

To extract valuable information from satellite data for applications such as agriculture, geological research, and environmental monitoring, the classification of hyperspectral images is an essential task. Labeling each pixel in this process is time-consuming and requires financial resources. To this end, working with a small number of samples is very important. In order to provide high classification performances with a limited number of samples, this paper aims to enhance the performance with an active learning framework. The framework incorporates dimensionality reduction, an edge-preserving filter, and active learning steps. From this perspective, we investigated different edge-preserving filter methods to analyze the effects on performance. By combining edge-preserving filters with dimensionality reduction, the study presents a unique method that improves classification performance while maintaining image quality and reducing noise. The following five edge-preserving smoothing filters are evaluated: weighted least squares (WLS), Joint-Histogram weighted median filter (Joint WMF), fast global image smoother (FGS), bilateral filter (BF), and static/dynamic (SD). Our experiments demonstrate that compared to the reference research (CNN+AL+MRF), the proposed framework increased overall and average accuracies about 2-5% for Indian Pines, Pavia University, and Salinas datasets.

Ground subsidence monitoring in based on UAV-LiDAR technology: a case study of a mine in the Ordos, China

Ground subsidence in Western coal mining areas is characterized by rapid deformation, extensive damage, and a wide range of impacts. The conventional observation methods are inappropriate for surface damage monitoring in high-intensity mining areas of Western China. Therefore, it is a crucial problem to quickly, accurately, and comprehensively monitor the ground subsidence and environmental damage caused by high-intensity and large-scale mining. In this study, we propose a monitoring method for the ground subsidence of high-intensity mining with Unmanned Aerial Vehicle Lidar (UAV-LiDAR) measurement technology. Taking a mine in Ordos of China as an example, the Digital Elevation Model (DEM) is obtained by Kriging Interpolation of the ground point cloud from UAV-LiDAR. Then, the multi-stage DEM differential processing is employed to get ground subsidence. Finally, the median and bilateral filters combine for denoise to obtain the high-precision ground subsidence. The results show that the accuracy of the ground DEM generated by UAV-LiDAR is 15 mm and the mean square error of the ground subsidence basin is 39 mm. UAV-LiDAR technology can quickly obtain abundant surface data and obtain high-precision ground subsidence. Therefore, the application of this technology and method in subsidence monitoring in mining areas is feasible. And it can provide support for ecological environment monitoring, land reclamation, and ecological restoration in mining areas. The research results can provide a useful basis for monitoring the surface damage of coal mining in Western China.

SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification.

Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification. This study aims to design a model to classify BC Histopathology images accurately by leveraging segmentation techniques. This work proposes a combined segmentation and classification approach for classifying BC using histopathology images to address these issues. Chan-Vese algorithm is used for segmentation to accurately delineate regions of interest within the histopathology images, followed by the proposed SegEIR-Net (Segmentation using EfficientNet, InceptionNet, and ResNet) for classification. Bilateral Filtering is also employed for noise reduction. The proposed model uses three significant networks, ResNet, InceptionNet, and EfficientNet, concatenates the outputs from each block followed by Dense and Dropout layers. The model is trained on the breakHis dataset for four different magnifications and tested on BACH (BreAst Cancer Histology) and UCSB (University of California, Santa Barbara) datasets. SegEIR-Net performs better than the existing State-of-the-Art (SOTA) methods in terms of accuracy on all three datasets, proving the robustness of the proposed model. The accuracy achieved on breakHis dataset are 98.66%, 98.39%, 97.52%, 95.22% on different magnifications, and 93.33% and 96.55% on BACH and UCSB datasets. These performance results indicate the robustness of the proposed SegEIR-Net framework in accurately classifying BC from histopathology images.

Residual U-Net approach for thyroid nodule detection and classification from thyroid ultrasound images

With so many thyroid knobs (nodules) discovered by accident, it is critical to recognize as many aberrant knobs (nodules) as possible from fine-needle aspiration (FNA) biopsies or other medical procedures while excluding those that are virtually certainly benign. Thyroid ultrasonography, on the other hand, is prone to interobserver variability and subjective translations. An effective deep learning model for segmenting and categorizing thyroid nodules in this study follows the stages below: data collection from a well-known archive, The Thyroid Digital Image Database (TDID), which comprises ultrasound pictures from 298 patients, preprocessing using anisotropic diffusion filter (ADF) for removing noise and enhancing the images, segmentation using a bilateral filter for segmenting images, feature extraction using grey level occurrence matrix (GLCM), feature selection using Multi-objective Particle Swarm with Random Forest Optimization (MbPSRA) and finally classification happens were Residual U-Net will be used. Experiment evaluation states the proposed model outperforms well than other state-of-art models.

Dual-Band Image Fusion Approach Using Regional Weight Analysis Combined with a Multi-Level Smoothing Filter

Image fusion is an effective and efficient way to express the feature information of an infrared image and abundant detailed information of a visible image in a single fused image. However, obtaining a fused result with good visual effect, while preserving and inheriting those characteristic details, seems a challenging problem. In this paper, by combining a multi-level smoothing filter and regional weight analysis, a dual-band image fusion approach is proposed. Firstly, a series of dual-band image layers with different details are obtained using smoothing results. With different parameters in a bilateral filter, different smoothed results are achieved at different levels. Secondly, regional weight maps are generated for each image layer, and then we fuse the dual-band image layers with their corresponding regional weight map. Finally, by imposing proper weights, those fused image layers are synthetized. Through comparison with seven excellent fusion methods, both subjective and objective evaluations for the experimental results indicate that the proposed approach can produce the best fused image, which has the best visual effect with good contrast, and those small details are preserved and highlighted, too. In particular, for the image pairs with a size of 640 × 480, the algorithm could provide a good visual effect result within 2.86 s, and the result has almost the best objective metrics.