Adaptive Weighted Mean Filter Research Articles

Modified U-Net based 3D reconstruction model to estimate volume from multi-view images of a solid object

ABSTRACT This paper proposes a modified U-Net based reconstruction model to automatically estimate the volume of solid objects. Initially, the captured multi-view images are pre-processed using improved adaptive weighted mean filter (IAWMF). Then, the pre-processed image is segmented using modified U-Net based learning mechanism to determine ROI. After segmentation, the key feature points are extracted using Harris corner detection approach. Further, the extracted feature keypoints are matched using Oriented FAST and Rotated BRIEF (ORB) approach and the similarity among two images is calculated using similarity detection algorithm. Then, the multiple image views from various angles are used to calculate the depth map using VGGNet-16 to attain the appropriate volume of the object. Finally, the 3D point clouds are reconstructed from multiple images using dense point cloud generation algorithm for volumetric analysis. Finally, the result shows that the proposed model provides better accuracy with less error than other 3D CAD model.

Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

Recently, the Internet of Medical Things (IoMT) has become a research hotspot due to its various applicability in medical field. However, the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment, generating a massive quantity of healthcare data. In such cases, cognitive computing can be employed that uses many intelligent technologies–machine learning (ML), deep learning (DL), artificial intelligence (AI), natural language processing (NLP) and others–to comprehend data expansively. Furthermore, breast cancer (BC) has been found to be a major cause of mortality among ladies globally. Earlier detection and classification of BC using digital mammograms can decrease the mortality rate. This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm (DL-MOMFO) for BC diagnosis and classification in the IoMT environment. The goal of this paper is to integrate deep learning (DL) and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC. The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter (AWMF)-based noise removal and contrast-limited adaptive histogram equalisation (CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms. In addition, a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms. Moreover, a SqueezeNet-based feature extraction and a fuzzy support vector machine (FSVM) classifier were used in the presented technique. To enhance the diagnostic performance of the presented method, the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques. The DL-MOMFO technique was tested on the MIAS database, and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.

Multiscale Random Convolution Broad Learning System for Hyperspectral Image Classification

Deep learning is a powerful technique for image processing. Convolution neural network (CNN) is one of the widely used approaches for hyperspectral image (HSI) classification. These methods mostly need a time-consuming pretraining process to obtain deep features. Random patches networks (RPNets) provide a novel approach that the convolution kernel can be the original image without any pretraining process. In this letter, we propose a novel HSI classification method, multiscale random convolution broad learning system (MRC-BLS), which takes the spatial feature learning by an adaptive weighted mean filter as the convolution kernel to extract local spatial feature in the first layer. Different sizes of random convolution kernels can obtain a multiscale feature map. The weighted fusion of multiscale spatial features extracted by different sizes kernels can get better performance in HSI classification. A broad learning system (BLS) is an efficient classifier to classify images by the multiscale random feature. Experiments in three HSI data sets fully testify to the efficiency and satisfactory performance of the proposed method.

Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal

In this study, we propose a new method, i.e. Adaptive Riesz Mean Filter (ARmF), by operationalizing pixel similarity for salt-and-pepper noise (SPN) removal. Afterwards, we compare the results of ARmF, A New Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Based on Pixel Density Filter (BPDF), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision-Based Algorithm (DBA) by using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Image Enhancement Factor (IEF), and Visual Information Fidelity (VIF) for 20 traditional test images (Lena, Cameraman, Barbara, Baboon, Peppers, Living Room, Lake, Plane, Hill, Pirate, Boat, House, Bridge, Elaine, Flintstones, Flower, Parrot, Dark-Haired Woman, Blonde Woman, and Einstein), 40 test images in the TESTIMAGES Database, and 200 RGB test images from the UC Berkeley Dataset ranging in noise density from 10% to 90%. Moreover, we compare the running time of these algorithms. These results show that ARmF outperforms the methods mentioned above. We finally discuss the need for further research.

An Iterative Mean Filter for Image Denoising

We propose an Iterative Mean Filter (IMF) to eliminate the salt-and-pepper noise. IMF uses the mean of gray values of noise-free pixels in a fixed-size window. Unlike other nonlinear filters, IMF does not enlarge the window size. A large size reduces the accuracy of noise removal. Therefore, IMF only uses a window with a size of $3\times3$ . This feature is helpful for IMF to be able to more precisely evaluate a new gray value for the center pixel. To process high-density noise effectively, we propose an iterative procedure for IMF. In the experiments, we operationalize Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity, Image Enhancement Factor, Structural Similarity (SSIM), and Multiscale Structure Similarity to assess image quality. Furthermore, we compare denoising results of IMF with ones of the other state-of-the-art methods. A comprehensive comparison of execution time is also provided. The qualitative results by PSNR and SSIM showed that IMF outperforms the other methods such as Based-on Pixel Density Filter (BPDF), Decision-Based Algorithm (DBA), Modified Decision-Based Untrimmed Median Filter (MDBUTMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Adaptive Type-2 Fuzzy Filter (FDS): for the IMAGESTEST dataset - BPDF (25.36/0.756), DBA (28.72/0.8426), MDBUTMF (25.93/0.8426), NAFSMF (29.32/0.8735), AWMF (32.25/0.9177), DAMF (31.65/0.9154), FDS (27.98/0.8338), and IMF (33.67/0.9252); and for the BSDS dataset - BPDF (24.95/0.7469), DBA (26.84/0.8061), MDBUTMF (26.25/0.7732), NAFSMF (27.26/0.8191), AWMF (28.89/0.8672), DAMF (29.11/0.8667), FDS (26.85/0.8095), and IMF (30.04/0.8753).

Fast adaptive and selective mean filter for the removal of high‐density salt and pepper noise

A fast adaptive and selective mean filter is presented to remove salt and pepper noise effectively from images corrupted with higher noise densities. The algorithm achieves better results in terms of visual quality and in terms of peak signal-to-noise ratio, mean absolute error, mean structural similarity index measure, image enhancement factor, and edge preservation ratio than many existing state-of-the-art algorithms at all noise densities. Adaptive filters that use variable window size produce better restoration of salt and pepper noise at higher noise densities than filters that use fixed window size, but they consume more time. This makes them practically impossible to implement them in digital image acquisition devices. Hence, reducing the execution time of adaptive filters is vital. The proposed algorithm consumes around 90% less time for lower noise densities and 50% less time for higher noise densities than the adaptive weighted mean filter, one of the best available adaptive filters in the literature for high-density salt and pepper noise removal.

Impulse noise detection technique based on fuzzy set

In this study, a new fuzzy-based technique is introduced for denoising images corrupted by impulse noise. The proposed method is based on the intuitionistic fuzzy set (IFS), in which the degree of hesitation plays an important role. The degree of hesitation of the pixels is obtained from the values of memberships of the object and the background of the image. After minimising the obtained hesitation function, the IFS is constructed and the noisy pixels are detected outside the neighbourhood of mean intensity of the object and the background of an image. Denoised images are relatively analysed with five other methods: modified decision-based unsymmetric trimmed median filter, noise adaptive fuzzy switched median filter, adaptive fuzzy switching weighted average filter, adaptive weighted mean filter, iterative alpha trimmed mean filter. Performances of the proposed method along with these five state-of the-art methods are evaluated using a peak signal-to-noise ratio and error rate along with the time for computation. Experimentally, derived denoising method showed an improved performance than five other existing techniques in filtering noise in images due to the reduction of uncertainty while choosing the noisy pixels.

A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise

In this letter, we propose a new adaptive weighted mean filter (AWMF) for detecting and removing high level of salt-and-pepper noise. For each pixel, we firstly determine the adaptive window size by continuously enlarging the window size until the maximum and minimum values of two successive windows are equal respectively. Then the current pixel is regarded as noise candidate if it is equal to the maximum or minimum values, otherwise, it is regarded as noise-free pixel. Finally, the noise candidate is replaced by the weighted mean of the current window, while the noise-free pixel is left unchanged. Experiments and comparisons demonstrate that our proposed filter has very low detection error rate and high restoration quality especially for high-level noise.

Remote sensing image fusion based on weighted filter empirical mode decomposition

Weighted filter empirical mode decomposition( WFEMD),as a new multi- scale and multi- resolution analysis algorithm,is more appropriate for the analysis of the image details than wavelet,super wavelet and dimensional empirical mode decomposition,and can solve the inherent defects of the traditional two- dimensional empirical mode decomposition( EMD). The main reason is that it directly computes the mean envelope by adaptive weighted mean filter. When WFEMD is introduced to the remote sensing image fusion,the characteristics of original images can be better extracted,and more information for fusion can be obtained. Firstly,the source images are decomposed by using WFEMD with the capability of acquirement of the high frequency data,the adaptability for some intrinsic mode functions( IMF) and the residual component,and then the IMFs and the residual component are fused with the details /background and average fusion regularity respectively at the corresponding scales.Finally,the fused IMFs and the residual component are reconstructed to obtain fusion results. Experiments have shown that the proposed algorithm is efficient in image fusion and is better than other current algorithms.

An effective 2-stage method for removing impulse noise in images

In this paper, a robust 2-stage impulse noise removal system is proposed to remove impulse noise from extremely corrupted images. The contributions are in two-fold. First, a neuro-fuzzy based impulse noise detector (NFIDET) is introduced to identify the noisy pixels. NFIDET is a powerful noise detector that can handle image corruption even up to 90% with zero miss and false detection rate with a simple neuro-fuzzy structure. This is the best result among the other impulse noise detectors in the literature. Second, this paper presents a new approach for weight calculation of adaptive weighted mean filter by using robust statistical model. An adaptive robust weighted mean (ARWM) filter removes a detected noisy pixel by adaptively determining filtering window size and replacing a noisy pixel with the weighted mean of the noise-free pixels in its window. A Geman–McClure robust estimation function is used to estimate the weights of the pixels. Simulation results also show that the proposed robust filter substantially outperforms many other existing algorithms in terms of image restoration.

Impulse Noise Removal Using Directional Difference Based Noise Detector and Adaptive Weighted Mean Filter

A two-stage algorithm, called switching-based adaptive weighted mean filter, is proposed to remove salt-and-pepper noise from the corrupted images. First, the directional difference based noise detector is used to identify the noisy pixels by comparing the minimum absolute value of four mean differences between the current pixel and its neighbors in four directional windows with a predefined threshold. Then, the adaptive weighted mean filter is adopted to remove the detected impulses by replacing each noisy pixel with the weighted mean of its noise-free neighbors in the filtering window. Numerous simulations demonstrate that the proposed filter outperforms many other existing algorithms in terms of effectiveness in noise detection, image restoration and computational efficiency.

영상에서 임펄스 잡음제거를 위한 적응력 있는 가중 평균 필터

본 논문은 영상을 획득할 때 잡음센서나 통신채널 불량으로 흔히 생기는 임펄스 잡음을 효율적으로 제거하는 방법에 대해 논의 하고자 한다. 제안된 방법은 잡음 픽셀 검출과 추정이라는 두 단계에 의해 이루어진다. 임펄스 잡음 검출기를 통하여 영상 전체에 걸쳐 잡음 픽셀여부를 검출한 후 잡음 픽셀로 판정되면 주변의 잡음 픽셀 개수에 따라 적응력 있게 <TEX>$3{\times}3$</TEX> 윈도우의 가중평균 혹은 <TEX>$5{\times}5$</TEX> 윈도우의 가중평균을 사용하여 추정한다. 제안된 방법의 성능을 평가하기 위해 영상실험을 통하여 기존의 잡음 제거 방법들과 정성적인 비교, PSNR과 MAE를 통한 정량적인 비교 그리고 수행 시간을 측정한 결과 제안된 방법은 잡음 제거는 물론 원영상에 대한 상세한 정보 보존력이 뛰어나고 수행 시간 면에서도 우수함을 보였다. In this work, a new adaptive weighted mean filter is proposed for preserving image details while effectively suppressing impulse noise. The proposed filter is based on a noise pixel detection-estimation strategy. All the pixels are first detected using an impulse noise detector. Then the detected noise pixels are replaced with the output of the weighted mean filter over adaptive working window according to the rate of corrupted neighborhood pixels, while noise-free pixels are left unaltered. We compare the proposed filter to other existing filters in the qualitative measure and quantitative measures such as PSNR and MAE as well as computation time to verify the capability of the proposed filter. Extensive simulations show that the proposed filter performs better than other filters in impulse noise suppression and detail preservation without increasing of running time.

Image restoration techniques based on fuzzy neural networks

By establishing some suitable partitions of input and output spaces, a novel fuzzy neural network (FNN) which is called selection type FNN is developed. Such a system is a multilayer feedforward neural network, which can be a universal approximator with maximum norm. Based on a family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN is constructed. As a result, the fusion of selection type FNN and inference type FNN results in a novel filter---FNN filter. It is simple in structure. And also it is convenient to design the learning algorithm for structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed on image and preserve fine image structure, simultaneously. Some examples are simulated to confirm the advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzy mean (AWFM) filter and so on, in suppression of noises and preservation of image structure.