Adaptive Bilateral Filter Research Articles

Adaptive pre-processing methods for increasing the accuracy of segmentation of dental X-RAY images

The subject of research in the article is the effectiveness of adaptive methods of preprocessing of medical images, in particular adaptive bilateral filter and modified CLAHE, in the tasks of segmentation of dental X-ray images. These methods make it possible to preserve important image details and effectively reduce noise, even in cases of high variability of images coming from different sources. The goal of the work is to study the impact of adaptive preprocessing methods on increasing the accuracy of segmentation of medical images and to determine the optimal combination of methods that provide the best results in segmentation tasks. The article addresses the following tasks: experimental comparison of adaptive preprocessing methods with traditional approaches, evaluation of segmentation efficiency using metrics such as Dice Score, Jacquard Coefficient (IoU Score), Precision and Sensitivity/Completeness (Recall)), as well as analysis of the effect of pre-processing on the quality of segmentation. The following methods are used: mathematical modeling, neural network training based on the U-Net model with a pre-trained timm-resnest101e encoder, image scaling to 512x512 pixels, training with a fixed learning rate of 0.001. The following results were obtained: the combined use of the adaptive bilateral filter and the modified CLAHE provided the highest segmentation quality indicators (Dice Score 0.9603 and Jacquard Coefficient (IoU Score) 0.94501), surpassing traditional methods. This proves the advantage of adaptive approaches in preserving the contours of objects and reducing noise. Conclusions: the application of adaptive preprocessing methods significantly improves the accuracy of segmentation of medical images. The combined approach including the adaptive bilateral filter and the modified CLAHE is the most effective for medical imaging tasks, which increases the accuracy of diagnosis and the reliability of automated decision support systems.

MEG AND PET IMAGES-BASED BRAIN TUMOR DETECTION USING KAPUR’S OTSU SEGMENTATION AND SOOTY OPTIMIZED MOBILENET CLASSIFICATION

Deep learning techniques have revolutionized medical image analysis recently, particularly in brain tumor (BT) detection. A comprehensive overview of the advancements and challenges associated with employing deep learning methodologies for the accurate and timely detection of BT. This work proposes a novel brain hybrid hexagonal mobile network (BH2Mnet) to identify benign and malignant tumors using MEG and PET images. An adaptive bilateral filter (ABF) is used as a de-noising for input images to eliminate noise artifacts. Eliminating the skull and outer cortical regions, a process known as "skull stripping" is utilized to enhance the number of training images. The de-noised images are segmented to detect the BT using Kapur's Otsu threshold (KOT) algorithm. Based on these segmented tumors, hexagonal feature sets with and without segmentation masks are produced using hybrid hexagonal features (HHF). Finally, the Sooty optimization-based MobileNet classifier is employed to classify the BT into benign, malignant cases. It was determined that the proposed BH2Mnet approach was 99.21 % accurate in classifying data. According to the proposed BH2Mnet NS-CNN, the total accuracy is enhanced by 1.67 %, 2.69 %, and 4.11 % compared to hybrid DAE, BFC, Deep CNN, and Neutrosophy.

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

Optical Sensor-Based Image Texture Characterization for Image Simulation Applications in College Gymnastics Instruction

Abstract Gymnastics learning is characterized by imitation and reference, and a teaching model combining optical sensors and image simulation systems may have promising results in college gymnastics courses. Based on the introduction of the meaning of college gymnastics teaching, the article sorted out the value performance of the image teaching mode and also established an optical three-dimensional imaging system based on optical sensors for gymnastics movement to obtain gymnastic images. In the optical 3D imaging system, the acquisition error of gymnastics images is reduced by using the finite distance image measurement method, and the quality of gymnastics images is enhanced by adaptive bilateral filtering and adaptive threshold selection of the Canny operator by the OTSU method. The enhanced gymnastics images are then inputted into a panoramic graphic imaging simulation system, and a gymnastics image simulation teaching model is established for colleges and universities. The effectiveness of the gymnastics image simulation teaching model is analyzed through a teaching comparison test conducted during simulation verification for optical imaging. In the optical imaging system, when the measurement distance is about 150m, the measurement error range is between [15″and 30″], and the MSE value of the gymnastic enhanced image obtained by the improved Canny algorithm decreases by 46.97% compared with the MSE value before the improvement. The students’ handstand score against the wall improved by 22.63 points compared to before the experiment, and more than 90% of the students were satisfied with the gymnastics image simulation teaching method. The teaching mode is constructed with the optical sensor to obtain the image characteristics of gymnastics movement, and the simulation system is innovative, which effectively promotes students’ interest in learning gymnastics courses and enhances their gymnastics training level.

Research on the Application of Behavioral Image Feature Capture in Basketball Game Video

<p>In order to realize intelligent image recognition of foul behavior in basketball games, this paper designs a feature capture method of video foul behavior based on improved bilateral filtering algorithm. Adaptive bilateral filtering is used to denoise the video image, and the optical flow feature and HOG feature of the behavior in the denoised image are obtained by image combination feature extraction method, which is fused to a combined eigenvector. The combined features were taken as the target recognition samples, and the multi-back propagation neural network was used to identify the foul behavior features. The particle filter was used to capture the video features and identify the location of the video behavior features. The experimental results show that this method can accurately capture the changes of video behavior characteristics, and has applicable performance in the identification of foul behavior in basketball matches.</p> <p> </p>

FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET

Ultrasound is a non-invasive method to diagnose and treat medical conditions. It is becoming increasingly popular to use portable ultrasound scanning devices to reduce patient wait times and make healthcare more convenient for patients. By using ultrasound imaging, you will be able to obtain images with better quality and also gain information about soft tissues. The interference caused by tissues reflected in ultrasound waves resulted in intensified speckle sound, complicating imaging. In this paper, a novel Foe-Net has been proposed for segmenting the fetal in ultrasound images. Initially, the input US images are noise removal phase using two different filters Adaptive Gaussian Filter (AGF) and Adaptive Bilateral Filter (ABF) used to reduce the noise artifacts. Then, the US images are enhanced using CLAHE and MSR for smoothing to enhance the image quality. Finally, the denoised images are input to the V-net is used to segment the fetal in the US images. The experimental outcomes of the proposed Multi-Scale Retinex (MSR) is an image enhancement technique that improves image quality by adjusting its illumination and enhancing details. Foe-Net was measured by specific parameters such as specificity, precision, and accuracy. The proposed Foe-Net achieves an overall accuracy of 99.48%, specificity of 98.56 %, and precision of 96.82 % for segmented fetal in ultrasound images. The proposed Foe-Net attains better pre-processing outcomes at low error rates and, high SNR, high PSNR, and high SSIM values.

Fetal Heart Disease Detection Via Deep Reg Network Based on Ultrasound Images

Congenital heart disease (CHD) is the most prevalent congenital ailment. One in every four newborns born with serious coronary artery disease will require surgery or other early therapy. Early identification of CHD in the fetal heart, on the other hand, is more critical for diagnosis. Extracting information from ultrasound (US) images is a difficult and time-consuming job. Deep learning (Dl) CNNs have been frequently utilized in fetal echocardiography for CAD identification to overcome this difficulty. In this work, a DL based neural network is proposed for classifying the normal and abnormal fetal heart based on US images. A total of 363 pregnant women between the ages of 18 and 34 weeks who had coronary artery disease or fetal good hearts were included. These US images are pre-processed using SCRAB (scalable range based adaptive bilateral filter) for eliminating the noise artifacts. The relevant features are extracted from the US images and classify them into normal and CHD by using the deep Reg net network. The proposed model integrates the Reg net -module with the CNN architecture to diminish the computational complexity and, simultaneously, attains an effectual classification accuracy. The proposed network attains higher accuracy of 98.4% for the normal and 97.2% for CHD. To confirm the efficiency of the proposed Reg net is compared to the various deep learning networks.

An integrated adaptive bilateral filter-based framework and attention residual U-net for detecting polycystic ovary syndrome

Introduction:PCOS is a global hormonal disorder in women, increasing the risk of certain cancers. Early diagnosis and treatment can manage it. While metabolic markers are typical for detection, MRI and ultrasound physiological features are gaining interest. Ultrasound is especially promising for early-stage detection. AI and ML are enhancing this detection process. Methodology:The methodology focuses on developing a hybrid model for detecting PCOS, an early indicator of gynecological cancer in women. This model incorporates adaptive bilateral filter-based image pre-processing and a novel attention residual u-net (AResUNet). The adaptive bilateral filtering allows for efficient noise reduction, while AResUNet’s architecture ensures adaptability across both 2D and multi-modal images. Findings:The findings of the study demonstrate a significant improvement in PCOS detection through the proposed hybrid model. It has been observed that the AResUNet model performs exceptionally well on both 2D and multi-modal images. Conclusion:The 2% improvement over current models reflects the innovation and potential applicability of this approach, providing a promising direction for further research and clinical practice in managing PCOS and other related health concerns.

Automated grape leaf nutrition deficiency disease detection and classification Equilibrium Optimizer with deep transfer learning model

ABSTRACT Our approach includes picture preprocessing, feature extraction utilizing the SqueezeNet model, hyperparameter optimisation utilising the Equilibrium Optimizer (EO) algorithm, and classification utilising a Stacked Autoencoder (SAE) model. Each of these processes is carried out in a series of separate steps. During the image preprocessing stage, contrast limited adaptive histogram equalisations (CLAHE) is utilized to improve the contrasts, and Adaptive Bilateral Filtering (ABF) to get rid of any noise that may be present. The SqueezeNet paradigm is utilized to obtain relevant characteristics from the pictures that have been preprocessed, and the EO technique is utilized to fine-tune the hyperparameters. Finally, the SAE model categorises the diseases that affect the grape leaf. The simulation analysis of the EODTL-GLDC technique tested New Plant Diseases Datasets and the results were inspected in many prospects. The results demonstrate that this model outperforms other deep learning techniques and methods that are more often related to machine learning. Specifically, this technique was able to attain a precision of 96.31% on the testing datasets and 96.88% on the training data set that was split 80:20. These results offer more proof that the suggested strategy is successful in automating the detection and categorization of grape leaf diseases.

Adaptive block level bilateral filtering algorithm

During the acquisition or transmission process, video images are subject to random signal interference and generate noise, which can hinder people's understanding of the image and subsequent processing work. Therefore, it is necessary to study video image denoising and filtering algorithms. Bilateral filter is one of many typical video image filtering algorithms. However, the traditional bilateral filter algorithm does not consider the differences in the contents of different regions of the image. It is difficult to obtain the optimal filtering effect by using a fixed filtering weight to filter the entire image, which leads to problems such as blurred image edges and inadequate details processing. This paper studies the influence of different filter block sizes on the bilateral filter effect, and proposes an algorithm to adaptively update the bilateral filter weight according to the block's variance. The experimental result shows that the performance of adaptive bilateral filter with different block sizes is expected to be better than that of traditional algorithms with fixed filter weights.

An improved deep learning network for AOD retrieving from remote sensing imagery focusing on sub-pixel cloud

ABSTRACT Following the success of MODIS, several widely used algorithms have been developed for different satellite sensors to provide global aerosol optical depth (AOD) products. Despite the progress made in improving the accuracy of satellite-derived AOD products, the presence of sub-pixel clouds and the corresponding cloud shadows still significantly degrade AOD products. This is due to the difficulty in identifying sub-pixel clouds, as they are hardly identified, which inevitably leads to the overestimation of AOD. To overcome these conundrums, we proposed an improved deep learning network for retrieving AOD from remote sensing imagery focusing on sub-pixel clouds especially and we call it the Sub-Pixel AOD network (SPAODnet). Two specific improvements considering sub-pixel clouds have been made; a spatial adaptive bilateral filter is applied to top-of-atmosphere (TOA) reflectance images for removing the noise induced by sub-pixel clouds and the corresponding shadows at the first place and channel attention mechanism is added into the convolutional neural network to further emphasize the relationship between the uncontaminated pixels and the ground measured AOD from AERONET sites. In addition, a compositive loss function, Huber loss, is used to further improve the accuracy of retrieved AOD. The SPAODnet model is trained by using ten AERONET sites within Beijing-Tianjin-Hebei (BTH) region in China, along with their corresponding MODIS images from 2011 to 2020; Subsequently, the trained network is applied over the whole BTH region and the AOD images over the BTH region from 2011 ~ 2020 are retrieved. Based on a comprehensive validation with ground measurements, the MODIS products, and the AOD retrieved from the other neural network, the proposed network does significantly improve the overall accuracy, the spatial resolution, and the spatial coverage of the AOD, especially for cases with sub-pixel clouds and cloud shadows.

Hybrid Pseudorandom Sequence for Broadband Impedance Measurements of Lithium-Ion Batteries

Broadband battery impedance spectroscopy brings valuable information that facilitates states estimation and fault diagnosis, which are of extensive research interests at present. pseudo random sequence (PRS) provides a promising tool for realizing online battery impedance measurements, yet it suffers from an insufficient signal-to-noise ratio (SNR). Motivated by this, this paper proposes a novel hybrid pseudo random sequence (HPRS) to boost the signal power in the low-frequency region, where the measurement data is exceptionally robust against noise corruption. The disturbances are further suppressed by applying a specially designed adaptive bilateral impedance filter (ABIF), which considers signal power content and impedance measurements&#x0027; distribution enabling a nearly unbiased result. The feasibility of the proposed HPRS signal is verified experimentally by comparing it with the typical PRS signal and step signal. The results suggest superior broadband impedance measurements&#x0027; accuracy and robustness under different ambient temperatures and state of charges (SOCs) and the maximum normalized root mean square error (NRMSE) is limited to 0.77<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>.

Edge-preserving smoothing filter using fast M-estimation method with an automatic determination algorithm for basic width

Digital filtering is essential for digital imaging, image recognition, and super-resolution technology. For example, the presence of noise in images captured by digital cameras causes deterioration of the image quality and image recognition rate. In order to improve the image recognition rate, noise reduction and edge preservation must be performed during preprocessing. Noise is generally reduced using low-pass filters, such as the Gaussian filter. Although they reduce noise, such filters also have the properties of blurring edge. A strong edge blur reduces the accuracy of the feature detection in image recognition. Therefore, in our previous study, a fast M-estimation Gaussian filter for images (FMGFI) was proposed as an image filter that simultaneously achieves denoising and edge preservation. In the FMGFI, the setting of the optimal basic width of the 2nd order B-spline basis functions is important for achieving simultaneous denoising and edge preservation. In this method, the optimal basic width of the FMGFI was determined not only by manually setting the basic width but also by human judgment of the filtered images. Consequently, the inability to automatically determine the optimal basic width hindered efficient denoising during image processing Therefore, in this research, we develop and propose a method that can automatically determine the optimal basic width of the FMGFI. The previously proposed method calculates using the same basic width for all the pixels over the entire image; in contrast, the proposed method calculates using the basic width automatically determined for each pixel. The experiments confirmed that the method proposed in this study achieves higher denoising and edge preservation performance than the ones used in previous research. The results also showed that it has the highest denoising performance against salt-and-pepper noise as compared to other filters: non-local mean filter, Gaussian filter, median filter, bilateral filter, adaptive bilateral filter, and FMGFI. The experimental results for the Gaussian noise sowed that the proposed method has the same denoising and edge preservation performance as the other filters in visual evaluation. From the above, the proposed method is expected to contribute to efficient denoising and improvement of image quality by using it as a preprocessing.

Phase noise suppression based on iterative adaptive bilateral filter in digital holographic microscopy for topography measurement

Topography measurement is a pivotal application in quantitative phase imaging using digital holographic microscopy. In this paper, a reflection-type digital holographic microscope is set up to reconstruct the phase image of the sample with step characteristics, and a noise suppression approach based on an iterative adaptive bilateral filter is proposed to reduce phase coherent noise. The proposed denoising approach considers the phase difference in the neighbourhood to better identify and preserve step characteristics. Experimental results demonstrated the effectiveness of the proposed method.

Automated Colonic Polyp Detection and Classification Enabled Northern Goshawk Optimization with Deep Learning

The major mortality factor relevant to the intestinal tract is the growth of tumorous cells (polyps) in various parts. More specifically, colonic polyps have a high rate and are recognized as a precursor of colon cancer growth. Endoscopy is the conventional technique for detecting colon polyps, and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate. The automated diagnosis of polyps in a computer-aided diagnosis (CAD) method is implemented using statistical analysis. Nowadays, Deep Learning, particularly through Convolution Neural networks (CNN), is broadly employed to allow the extraction of representative features. This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification (NGOTL-CPDC) model. The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection. To accomplish this, the NGOTL-CPDC technique comprises of adaptive bilateral filtering (ABF) technique as a noise removal process and image pre-processing step. Besides, the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer. Finally, the fuzzy Hopfield neural network (FHNN) method can be employed for colonic poly detection and classification. A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model. The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.

Feature Correlated Auto Encoder Method for Industrial 4.0 Process Inspection Using Computer Vision and Machine Learning

From the perspective of the Industry 4.0 paradigm, the machine learning (ML) discipline has had a significant influence on the manufacturing sector. The industry 4.0 concept promotes intelligent sensors, gadgets, and equipment to create technology infrastructure sectors that collect information constantly. By analyzing the obtained data, machine learning approaches allow actionable insight to boost industrial productivity without dramatically altering the necessary resources. Furthermore, the capacity of machine learning applications to provide actionable analytics has facilitated the detection of complex manufacturing trends and paved the path for an integrated intelligent process in various activities in the supply chain, including smart and constant inspection, preventative maintenance, quality enhancement, process optimization, supply chain advancement, and workflow scheduling. This paper aims to present recent advances in the field of quality inspection in Industry 4.0 and develop a framework for quality inspection that can be fully utilized in the Industry 4.0 context using adaptive bilateral filtering and Feature Correlated Auto encoder (FCA) machine learning technique. The suggested approach makes full use of information from all sources along the manufacturing chain. Therefore, it complies with quality management standards within the context of Industry 4.0. The suggested model makes use of corrective measures based on data patterns discovered through predictive analysis. Result analysis was shown on some pre-trained deep learning models such as ResNet18, Vgg19, Alexnet, Squeezenet, auto encoder, and FCA and observed that the proposed FCA(Feature Correlated Auto encoder) achieved a better result.

ABF de-hazing algorithm based on deep learning CNN for single I-Haze detection

Haze is a kind of natural event which is formed by smoke, dust, or other dry particles. It acts as an overshot-on scene and degrades the visibility of environments and damages the image quality of indoor pictures due to the haze changing colors. Therefore, the importance of removing the haze from the images is increased in the field of computer graphics or vision. Because of its obscurity in mathematical formation, the removal process of haze becomes very complex and it is also made more difficult when the image is in a form of a purely single image. Here, the single-image haze removal process is the most complicated operation because of its ill-posed behavior. In this paper, the authors proposed a powerful single-image haze removal and recognition process. An Adaptive Bilateral Filter (ABF) is designed using an optimal selection of spatial weight parameters for de-hazing the scene depth of the hazy image. It is essential to classify both the haze and non-haze images because, without performing detection tasks, the non-haze images are also fed into the de-hazing process and lead to system complexity. Henceforth for detection purposes, a deep Convolution Neural Network is proposed to categorize the haze and non-haze images. Here deep CNN is proposed for recovering the depth information in an input image using a network training and testing model. The simulation results are demonstrating that the presented technique performed very well in the identification of the de-hazing effect and efficiency of haze removal algorithms.

SAR Ship Image Speckle Noise Suppression Algorithm Based on Adaptive Bilateral Filter

Aiming at the shortcomings of traditional bilateral filtering in suppressing speckle noise in SAR ship images, especially strong speckle noise and loss of image edge details, it proposes an improved bilateral filtering algorithm based on fast adaptive threshold and variable window in this paper. The algorithm was used to suppress speckle noise in SAR ship images. The traditional bilateral filtering cannot effectively filter out the strong speckle noise, but the SAR image has strong speckle noise because of the defects of its imaging principle. To solve these problems, an image sample truncation method based on fast adaptive truncation statistical characteristics is designed to adjust the photometric similarity weight characteristics to realize the adaptive adjustment of spatial standard deviation and gray standard deviation. After the local reference window is modified and truncated according to the local characteristics of the image, the adjusted combined similarity weight value greatly reduces the impact of strong speckle noise on the image. It is smoothed into speckle signal with strong impulse noise. In the traditional bilateral filtering, in order to enhance the effect of smoothing noise, it is necessary to specify a large value of geometric diffusion factor and gray similarity diffusion factor, resulting in the loss of image details. Based on the variable window size filtering method, when the extended local reference window is in the case of nonuniform edge, its window can be enlarged to make the speckle noise stronger. When an extended window contains details such as edges and textures, its size is not expanded to maintain image detail. This method can further smooth the speckle noise in the uniform region while preserving the edge details of the image. Finally, the adaptive truncated sample is used as the input of the bilateral filter. The image sample truncation method based on fast adaptive threshold can effectively eliminate the strong speckle noise information that affects the photometric similarity and weight accuracy of the image. The method based on variable window can greatly enhance the smoothness of the edge area of the image. The experimental results show that improved adaptive bilateral filtering algorithm improves the speckle noise removal ability by 16.06% compared with the traditional bilateral filtering algorithm in the speckle noise suppression of the SAR ship image, and the preservation performance of the image edge after filtering is improved by 5.41%. Compared with the original image, the filtered image has a 1.2% improvement in structural similarity. The algorithm can effectively suppress speckle noise and has a good ability to retain edge and texture information of the SAR ship image, which has strong practicability.

Molecular cancer classification on microarrays gene expression data using wavelet‐based deep convolutional neural network

AbstractMicroarray data analysis is a most promising and difficult process due to the complex nature of data. It includes higher dimensionality, several unbalanced classes, a smaller sample size, the presence of noise, and a higher variation of feature values. This has resulted in a decrease in classification accuracy as well as an overfitting problem. This work proposed an efficient and hybrid deep learning technique for molecular cancer classification using expression data to solve these limitations. The different steps in the proposed work are preprocessing, clustering, extraction, selection, and classification. The input data is preprocessed using a scalable range adaptive bilateral filter. Then clustering is done with the help of an improved binomial clustering approach. After that, the data is extracted with the help of the multifractal Brownian motion method. Then the important features are selected with the help of an improved cuckoo search optimization algorithm. Finally, the data classification is performed using a wavelet‐based deep convolutional neural network. This work is validated with the help of five publically available datasets using the PYTHON platform. The different performance measures considered here are accuracy, precision, recall, and F‐measure. The classification accuracy obtained is 98.36%, 98.12%, 98.55%, 97.70%, and 95.30% for ovarian, breast, colon, leukemia, and prostate cancer datasets. The overall result showed that the suggested technique is better than the existing methods.

FPGA implementation of hybrid recursive reversable box filter-based fast adaptive bilateral filter for image denoising

A Bilateral filter (BF) is a kind of edge-preserving filter, which is mostly used in computer vision, computational photography, and image processing applications. Recently, Reversible Logic Gates (RLG) technology has emerged as an attractive area in the field of field-programmable gate array (FPGA) based implementations. The RLG technology consumes less Quantum Cost (QC) as compared to conventional CMOS-based logic gates. As a result, this paper proposed an FPGA implementation of a hybrid recursive reversible box filter (HRRBF)-based fast adaptive bilateral filter (FABF) using RLG technology with joint geometric and photometric weight calculation and kernel result calculation operations. The RLGs are used to develop the reversable modified carry select adder (RMCSA), reversable adder subtractor (RAS), and multiplier, which significantly improves the speed and reduces the execution time, area, power, and delay as compared to the basic methods. In addition, ternary content addressable memory (TCAM) is used as the main memory to improve the speed of filtering. Obtained hardware and simulation results show that proposed RLG-based HRRBF-FABF results in enhanced performance as compared to state-of-the-art architectures. In addition, the image denoising application was also implemented with the proposed RLG-based HRRBF-FABF and achieved structural similarity (SSIM) index of 0.999 and peak signal-to-noise ratio (PSNR) of 78.348 dB. The simulation results showed that the proposed method resulted in better denoising performance as compared to conventional filtering approaches.