scholarly journals 3D-generalization of impulse noise removal method for video data processing

2020 ◽  
Vol 44 (1) ◽  
pp. 92-100
Author(s):  
N.I. Chervyakov ◽  
P.A. Lyakhov ◽  
A.R. Orazaev
Keyword(s):  
Data Processing ◽  
Video Processing ◽  
Impulse Noise ◽  
Structural Similarity ◽  
Noise Removal ◽  
Video Data ◽  
Practical Applications ◽  
Iterative Processing ◽  
And Control

The paper proposes a generalized method of adaptive median impulse noise filtering for video data processing. The method is based on the combined use of iterative processing and transformation of the result of median filtering based on the Lorentz distribution. Four different combinations of algorithmic blocks of the method are proposed. The experimental part of the paper presents the results of comparing the quality of the proposed method with known analogues. Video distorted by impulse noise with pixel distortion probabilities from 1% to 99% inclusive was used for the simulation. Numerical assessment of the quality of cleaning video data from noise based on the mean square error (MSE) and structural similarity (SSIM) showed that the proposed method shows the best result of processing in all the considered cases, compared with the known approaches. The results obtained in the paper can be used in practical applications of digital video processing, for example, in systems of video surveillance, identification systems and control of industrial processes.

Bayes Performance of Batch Data Mining Based on Functional Dependencies

2019 ◽  
Vol 33 (03) ◽  
pp. 1959011
Author(s):  
Haixu Xi ◽  
Feiyue Ye ◽  
Sheng He ◽  
Yijun Liu ◽  
Hongfen Jiang
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Video Processing ◽  
Mining Area ◽  
Batch Processing ◽  
Video Data ◽  
Batch Processes ◽  
Functional Dependencies ◽  
Workflow System ◽  
Traffic Video

Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

scholarly journals Image Enhancement and Denoising in Extreme Low-Light Conditions

2019 ◽  
Vol 9 (1) ◽  
pp. 5259-5264
Keyword(s):  
Image Enhancement ◽  
Smart Phone ◽  
Image Data ◽  
Structural Similarity ◽  
Noise Removal ◽  
Low Light ◽  
Daily Experience ◽  
Quantitative Results ◽  
Loss Term

Image noise refers to the specks of false colors or artifacts that diminish the visual quality of the captured image. It has become our daily experience that with affordable smart-phone cameras we can capture high clarity photos in a brightly illuminated scene. But using the same camera in a poorly lit environment with high ISO settings results in images that are noisy with irrelevant specks of colors. Noise removal and contrast enhancement in images have been extensively studied by researchers over the past few decades. But most of these techniques fail to perform satisfactorily if the images are captured in an extremely dark environment. In recent years, computer vision researchers have started developing neural network-based algorithms to perform automated de-noising of images captured in a low-light environment. Although these methods are reasonably successful in providing the desired de-noised image, the transformation operation tends to distort the structure of the image contents to a certain extent. We propose an improved algorithm for image enhancement and de-noising using the camera’s raw image data by employing a deep U-Net generator. The network is trained in an end-to-end manner on a large training set with suitable loss functions. To preserve the image content structures at a higher resolution compared to the existing approaches, we make use of an edge loss term in addition to PSNR loss and structural similarity loss during the training phase. Qualitative and quantitative results in terms of PSNR and SSIM values emphasize the effectiveness of our approach.

Adaptive Trimmed Median Filter for Impulse Noise Detection and Removal with an Application to Mammogram Images

2020 ◽  
Vol 20 (04) ◽  
pp. 2050032
Author(s):  
Rubul Kumar Bania ◽  
Anindya Halder
Keyword(s):  
Breast Cancer ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Statistical Significance ◽  
Similarity Index ◽  
Structural Similarity ◽  
Noise Removal ◽  
Quantitative Indices ◽  
Mammogram Images

Mammography imaging is one of the most widely used techniques for breast cancer screening and analysis of abnormalities. However, due to some technical difficulties during the time of acquisition and digital storage of mammogram images, impulse noise may be present. Therefore, detection and removals of impulse noise from the mammogram images are very essential for early detection and further diagnosis of breast cancer. In this paper, a novel adaptive trimmed median filter (ATMF) is proposed for impulse noise (salt & pepper (SNP)) detection and removal with an application to mammogram image denoising. Automatic switching mechanism for updating the Window of Interest (WoI) size from ([Formula: see text]) to ([Formula: see text]) or ([Formula: see text]) is performed. The proposed method is applied on publicly available mammogram images corrupted with varying SNP noise densities in the range 5%–90%. The performance of the proposed method is measured by various quantitative indices like peak signal to noise ratio (PSNR), mean square error (MSE), image enhancement factor (IEF) and structural similarity index measure (SSIM). The comparative analysis of the proposed method is done with respect to other state-of-the-art noise removal methods viz., standard median filter (SMF), decision based median filter (DMF), decision based unsymmetric trimmed median filter (DUTMF), modified decision based unsymmetric trimmed median filter (MDUTMF) and decision based unsymmetric trimmed winsorized mean filter (DUTWMF). The superiority of the proposed method over other compared methods is well evident from the experimental results in terms of the quantitative indices (viz., PSNR, IEF and SSIM) and also from the visual quality of the denoised images. Paired t-test confirms the statistical significance of the higher PSNR values achieved by the proposed method as compared to the other counterpart techniques. The proposed method turned out to be very effective in denoising both high and low density noises present in (mammogram) images.

scholarly journals A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Hongyao Deng ◽  
Qingxin Zhu ◽  
Xiuli Song ◽  
Jinsong Tao
Keyword(s):  
Total Variation ◽  
Impulse Noise ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Diffusion Method ◽  
Median Filtering ◽  
Noise Characteristics ◽  
Weighted Total Variation ◽  
Salt And Pepper

Impulsive noise removal usually employs median filtering, switching median filtering, the total variation L1 method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images.

scholarly journals Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Di Guo ◽  
Xiaobo Qu ◽  
Xiaofeng Du ◽  
Keshou Wu ◽  
Xuhui Chen
Keyword(s):  
Sparse Representation ◽  
Impulse Noise ◽  
Structural Similarity ◽  
Noise Removal ◽  
Noise Detection ◽  
Noise Levels ◽  
Denoising Method ◽  
Transmission Errors ◽  
Alternating Direction ◽  
Salt And Pepper

Images may be corrupted by salt and pepper impulse noise due to noisy sensors or channel transmission errors. A denoising method by detecting noise candidates and enforcing image sparsity with a patch-based sparse representation is proposed. First, noise candidates are detected and an initial guide image is obtained via an adaptive median filtering; second, a patch-based sparse representation is learnt from this guide image; third, a weightedl1-l1regularization method is proposed to penalize the noise candidates heavier than the rest of pixels. An alternating direction minimization algorithm is derived to solve the regularization model. Experiments are conducted for 30%∼90% impulse noise levels, and the simulation results demonstrate that the proposed method outperforms total variation and Wavelet in terms of preserving edges and structural similarity to the noise-free images.

Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function

2016 ◽  
Vol 6 (5) ◽  
pp. 2219
Author(s):  
Suhad A. Ali ◽  
C. Elaf A. Abbood ◽  
Shaymaa Abdu LKadhm
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Different Types ◽  
Estimation Function ◽  
Salt And Pepper ◽  
Gaussian Estimation

<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

scholarly journals A service for monitoring the quality of intraoperative cone beam CT images

2016 ◽  
Vol 2 (1) ◽  
pp. 373-377
Author(s):  
Frank Heckel ◽  
Jan Schlamelcher ◽  
Hanna Rotermund ◽  
Max Rockstroh ◽  
Stephan Zidowitz ◽  
...  
Keyword(s):  
Data Processing ◽  
Image Quality Assessment ◽  
Cone Beam Ct ◽  
Image Data ◽  
Operating Rooms ◽  
Cone Beam ◽  
Service Component ◽  
And Control ◽  
Exchange Data

AbstractIn recent years, operating rooms (ORs) have transformed into integrated operating rooms, where devices are able to communicate, exchange data, or even steer and control each other. However, image data processing is commonly done by dedicated workstations for specific clinical use-cases. In this paper, we propose a concept for a dynamic service component for image data processing on the example of automatic image quality assessment (AQUA) of intraoperative cone beam computed tomography (CBCT) images. The service is build using the Open Surgical Communication Protocol (OSCP) and the standard for Digital Imaging and Communications in Medicine (DICOM). We have validated the proposed concept in an integrated demonstrator OR.

scholarly journals Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function

2016 ◽  
Vol 6 (5) ◽  
pp. 2219
Author(s):  
Suhad A. Ali ◽  
C. Elaf A. Abbood ◽  
Shaymaa Abdu LKadhm
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Different Types ◽  
Estimation Function ◽  
Salt And Pepper ◽  
Gaussian Estimation

<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

scholarly journals Human vision system-based structural similarity model for evaluating seismic image quality

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. F49-F54
Author(s):  
Ning Yang ◽  
Yi Duan
Keyword(s):  
Data Processing ◽  
Vision System ◽  
Structural Similarity ◽  
Human Vision ◽  
Subjective Perception ◽  
Seismic Section ◽  
Seismic Image ◽  
Before And After ◽  
Seismic Sections

The quality of a seismic section is generally evaluated based on relevant qualitative and quantitative criteria. We have developed a new quantitative model to evaluate seismic sections, i.e., a full-reference human visual system based on seismic image structural similarity. With this model, we compared and analyzed two seismic sections before and after data processing in terms of energy intensity, contrast, and seismic reflection configuration similarity measures. We evaluated the quality of the seismic section in combination with the three measures, and their changing trends before and after data processing are represented with relevant quantitative indicators. The numerical simulation used algorithms and actual data, and the results indicate that compared with conventional evaluation methods, our method is easier to understand and simpler to calculate. Our method can also highlight the differences between changing seismic sections and improve the consistency of the objective evaluation results of seismic images with the corresponding human subjective perception.

scholarly journals Impulse Noise Removal Based on Hybrid Genetic Algorithm

2021 ◽  
Vol 38 (4) ◽  
pp. 1245-1251
Author(s):  
Nail Alaoui ◽  
Arwa Mashat ◽  
Amel Baha Houda Adamou-Mitiche ◽  
Lahcène Mitiche ◽  
Aicha Djalab ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Hybrid Genetic Algorithm ◽  
Structural Similarity ◽  
Noise Removal ◽  
Impulse Noise Removal ◽  
Salt And Pepper ◽  
Crossover And Mutation ◽  
Different Levels

In this paper, we introduce a new method, impulse noise removal based on hybrid genetic algorithm (INRHGA) to remove impulse noise at different noise densities of noise while preserving the main features of the image. The proposed approach merges the genetic algorithm and methods for filtering images that are combined into the population as essential solutions to create a developed and improved population. A set of individuals is developed into a number of iterations using factors of crossover and mutation. Our method develops a group of images instead of a set of parameters from the filters. We then introduced some of the concepts and steps of it. The proposed algorithm is compared with some image denoising algorithm. By using Peak Signal to Noise Ratio (PSNR), structural similarity (SSIM). For example, for Lenna image with 60% salt and pepper noise density, PSNR, SSIM results of AMF, MDBUTMFG and NAFSM methods are 20,39/ 28.74/ 29.85 and 0.5679/ 0.8312/ 0.8818 respectively, while PSNR, SSIM results of the proposed algorithm are 29.92 and 0.8838, respectively. Experimental results indicate that INRHGA is very effective and visually comparable with the above-mentioned methods at different levels of noise.

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