A Mixed Noise Filtering Algorithm for High-Speed Sequence Image Processing

2013 ◽  
Vol 415 ◽  
pp. 318-324
Author(s):  
Yan Zhu Yang ◽  
Wei Liang Liu ◽  
Neng Jie Chen ◽  
Wei Zhu
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
High Speed ◽  
Impulse Noise ◽  
Effective Means ◽  
Weighted Average ◽  
Frame Rate ◽  
Good Effect ◽  
Filtering Algorithm ◽  
Mixed Noise

t is a effective means by using high-speed vision to locate mobile targets. Under the circumstance of high frame rate and high sensitivity (300Hz), in addition to the Gaussian noise and impulse noise, the image quality is also influenced by atmospheric instability, and it is mainly expressed as Gaussian noise. An improved adaptive threshold weighted mean (IATWM) de-noising algorithm is proposed in this paper. According to the characteristics of impulse noise, the algorithm is able to obtain the threshold adaptively and separate the impulse noise. Then, the weighted median filtering algorithm is used to remove the impulse noise. And the improved weighted average filter algorithm is adopted to remove the Gaussian noise for graphics with Gaussian noise. The algorithm could deal with the Gaussian noise and impulse noise separately, avoiding the weaken handling for the parts not subject to pixels pollution of the impulse noise. The experimental results show that the processing result of the algorithm is able to retain the image details, superior to the traditional filtering algorithms and MTM algorithm. In addition, the algorithm provides an effective way to eliminate the mixed noise, along with a good effect on the high-speed sequence image processing.

scholarly journals A portable smartphone-based laryngoscope system for high-speed vocal cord imaging of patients with throat disorders (Preprint)

2020 ◽  
Author(s):  
Jun Ki Kim ◽  
Youngkyu Kim ◽  
Jungmin Oh ◽  
Seung-Ho Choi ◽  
Ahra Jung ◽  
...  
Keyword(s):  
Image Processing ◽  
Vocal Cord ◽  
High Speed ◽  
High Performance ◽  
Vocal Folds ◽  
Frame Rate ◽  
Endoscopic Imaging ◽  
High Speed Imaging ◽  
Underdeveloped Countries ◽  
Longitudinal Edge

BACKGROUND Recently, high-speed digital imaging (HSDI), especially HSD endoscopic imaging is being routinely used for the diagnosis of vocal fold disorders. However, high-speed digital endoscopic imaging devices are usually large and costly, which limits access by patients in underdeveloped countries and in regions with inadequate medical infrastructure. Modern smartphones have sufficient functionality to process the complex calculations that are required for processing high-resolution images and videos with a high frame rate. Recently, several attempts have been made to integrate medical endoscopes with smartphones to make them more accessible to underdeveloped countries. OBJECTIVE To develop a smartphone adaptor for endoscopes to reduce the cost of devices, and to demonstrate the possibility of high-speed vocal cord imaging using the high-speed imaging functions of a high-performance smartphone camera. METHODS A customized smartphone adaptor was designed for clinical endoscopy using selective laser melting (SLM)-based 3D printing. Existing laryngoscope was attached to the smartphone adaptor to acquire high-speed vocal cord endoscopic images. Only existing basic functions of the smartphone camera were used for HSDI of the vocal folds. For image processing, segmented glottal areas were calculated from whole HSDI frames, and characteristics such as volume, shape and longitudinal edge length were analyzed. RESULTS High-speed digital smartphone imaging with the smartphone-endoscope adaptor could achieve 940 frames per second, and was used to image the vocal folds of five volunteers. The image processing and analytics demonstrated successful calculation of relevant diagnostic variables from the acquired images. CONCLUSIONS A smartphone-based HSDI endoscope system can function as a point-of-care clinical diagnostic device. Furthermore, this system is suitable for use as an accessible diagnostic method in underdeveloped areas with inadequate medical service infrastructure.

Mixed Noise Image De-Noising Based on EM Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4734-4741 ◽  
Author(s):  
Gui Cun Shi ◽  
Fei Xing Wang
Keyword(s):  
Applied Sciences ◽  
Wavelet Analysis ◽  
Em Algorithm ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Noise Source ◽  
High Quality ◽  
Noise Image ◽  
Mixed Noise ◽  
Non Gaussian

Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model’s parameter estimation is a key issue, because the accuracy of the noise model’s parameters could affect the de-noising quality. In the case of mixed Gaussian noises, EM algorithm is an iterative algorithm, which simplifies the maximum likelihood equation. This thesis takes wavelet analysis and statistics theory as tools, studies on mixed noise image de-noising, provides two classes of algorithms for dealing with a special type of non-Gaussian noise, mixed Gaussian and Pepper & Salt noise.

scholarly journals WindSTORM: Robust online image processing for high-throughput nanoscopy

2019 ◽  
Vol 5 (4) ◽  
pp. eaaw0683 ◽  
Author(s):  
Hongqiang Ma ◽  
Jianquan Xu ◽  
Yang Liu
Keyword(s):  
Image Processing ◽  
High Throughput ◽  
High Speed ◽  
High Density ◽  
Frame Rate ◽  
Large Field ◽  
Localization Accuracy ◽  
Image Characteristics ◽  
Emitter Localization ◽  
Online Image Processing

High-throughput nanoscopy becomes increasingly important for unraveling complex biological processes from a large heterogeneous cell population at a nanoscale resolution. High-density emitter localization combined with a large field of view and fast imaging frame rate is commonly used to achieve a high imaging throughput, but the image processing speed and the presence of heterogeneous background in the dense emitter scenario remain a bottleneck. Here, we present a simple non-iterative approach, referred to as WindSTORM, to achieve high-speed high-density emitter localization with robust performance for various image characteristics. We demonstrate that WindSTORM improves the computation speed by two orders of magnitude on CPU and three orders of magnitude upon GPU acceleration to realize online image processing, without compromising localization accuracy. Further, WindSTORM is highly robust to maximize the localization accuracy and minimize the image artifacts in the presence of nonuniform background. WindSTORM paves the way for next generation high-throughput nanoscopy.

scholarly journals Processing of high-speed videos of shock wave boundary layer interactions

2020 ◽  
Author(s):  
Samee Maharjan ◽  
Dag Bjerketvedt ◽  
Ola Marius Lysaker
Keyword(s):  
Boundary Layer ◽  
Shock Wave ◽  
Image Processing ◽  
High Speed ◽  
Frame Rate ◽  
Image Size ◽  
Pressure Transducers ◽  
Reflected Shock ◽  
Shock Position ◽  
Wave Boundary

Abstract This paper presents a framework for processing high-speed videos recorded during gas experiments in a shock tube. The main objective is to study boundary layer interactions of reflected shock waves in an automated way, based on image processing. The shock wave propagation was recorded at a frame rate of 500,000 frames per second with a Kirana high-speed camera. Each high-speed video consists of 180 frames, with image size [$$768 \times 924$$ 768 × 924 ] pixels. An image processing framework was designed to track the wave front in each image and thereby estimate: (a) the shock position; (b) position of triple point; and (c) shock angle. The estimated shock position and shock angle were then used as input for calculating the pressure exerted by the shock. To validate our results, the calculated pressure was compared with recordings from pressure transducers. With the proposed framework, we were able to identify and study shock wave properties that occurred within less than $$300\, \upmu \hbox {sec}$$ 300 μ sec and to track evolveness over a distance of 100 mm. Our findings show that processing of high-speed videos can enrich, and give detailed insight, to the observations in the shock experiments.

Design and Implementation of a High-Speed LVDS Data Acquisition System Based on Virtex-5 FPGA

2014 ◽  
Vol 568-570 ◽  
pp. 193-197
Author(s):  
Qiang Wu ◽  
Gen Wang ◽  
Xu Wen Li
Keyword(s):  
Image Processing ◽  
Data Acquisition ◽  
High Speed ◽  
Image Data ◽  
Image Transmission ◽  
Data Acquisition System ◽  
Frame Rate ◽  
Acquisition System ◽  
Simulation Tools ◽  
Impedance Calculation

A High-Speed LVDS Data Acquisition system is designed, with XILINX’s Virtex-5 FPGA as core processor as well as TI’s TMS320C6748 DSP for pre-processing and storing data. This system achieved a greater amount of image processing and faster image processing requirement. The system completed the dual LVDS image data acquisition according to the demand. The resolution of the image data is 320x257. Each image transmission frame rate of not less than 150 / sec. large amount of data throughout the system as well as real-time demanding is a big challenge for designer. The designer uses simulation tools from Mentor Graphics Hyperlynx to complete the stack and impedance calculation and signal quality simulation to ensure that the system is stable and reliable. This system also has better scalability and more reliable storage method than past designs. Recently, the system has completed testing verification and results show that this design is feasible and reliable.

An Image Filtering Algorithm for Removing Mixed Noise

2013 ◽  
Vol 433-435 ◽  
pp. 383-388 ◽  
Author(s):  
Mao Xiang Chu ◽  
An Na Wang ◽  
Rong Fen Gong
Keyword(s):  
Gaussian Noise ◽  
Weighting Function ◽  
Median Filter ◽  
Bilateral Filter ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Noise Simulation ◽  
Mixed Noise ◽  
Local Edge ◽  
Salt And Pepper

In order to remove salt-and-pepper noise and Gaussian noise in image, a novel filtering algorithm is proposed in this paper. The novel algorithm can preserve image edge details as much as possible. Firstly, five-median-binary code (FMBC) is proposed and used to describe local edge type of image. Secondly, median filter algorithm is improved to remove salt-and-pepper noise by using FMBC. Then, local enhanced bilateral filter with FMBC and a new type of exponential weighting function is used to remove Gaussian noise. Simulation results show that the algorithm proposed in this paper is very effective not only in filtering mixed noise but also in preserving edge details.

scholarly journals Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

2018 ◽  
Vol 10 (10) ◽  
pp. 1600 ◽  
Author(s):  
Chang Li ◽  
Yu Liu ◽  
Juan Cheng ◽  
Rencheng Song ◽  
Hu Peng ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Additive White Gaussian Noise ◽  
Bilinear Model ◽  
Hyperspectral Unmixing ◽  
Noise Impulse ◽  
Mixed Noise ◽  
Alternative Direction Method

Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.

scholarly journals Multi-Target Tracking Using a Vision Chip and its Applications to Real-Time Visual Measurement

2005 ◽  
Vol 17 (2) ◽  
pp. 121-129 ◽  
Author(s):  
Yoshihiro Watanabe ◽  
◽  
Takashi Komuro ◽  
Shingo Kagami ◽  
Masatoshi Ishikawa
Keyword(s):  
Image Processing ◽  
Target Tracking ◽  
Real Time ◽  
High Speed ◽  
Vision System ◽  
Visual Observation ◽  
Frame Rate ◽  
Vision Chip ◽  
Visual Measurement ◽  
Multi Target Tracking

Real-time image processing at high frame rates could play an important role in various visual measurement. Such image processing can be realized by using a high-speed vision system imaging at high frame rates and having appropriate algorithms processed at high speed. We introduce a vision chip for high-speed vision and propose a multi-target tracking algorithm for the vision chip utilizing the unique features. We describe two visual measurement applications, target counting and rotation measurement. Both measurements enable excellent measurement precision and high flexibility because of high-frame-rate visual observation achievable. Experimental results show the advantages of vision chips compared with conventional visual systems.

scholarly journals WindSTORM: Robust online image processing for high-throughput nanoscopy

2018 ◽  
Author(s):  
Hongqiang Ma ◽  
Jianquan Xu ◽  
Yang Liu
Keyword(s):  
Image Processing ◽  
High Throughput ◽  
High Speed ◽  
High Density ◽  
Frame Rate ◽  
Large Field ◽  
Localization Accuracy ◽  
Uniform Background ◽  
Emitter Localization ◽  
Online Image Processing

AbstractHigh-throughput nanoscopy becomes increasingly important for unraveling complex biological processes from a large heterogeneous cell population at a nanoscale resolution. High-density emitter localization combined with a large field of view and fast imaging frame rate is commonly used to achieve a high imaging throughput, but the image processing speed in the dense emitter scenario remains a bottleneck. Here we present a simple non-iterative approach, referred to as WindSTORM, to achieve high-speed high-density emitter localization with robust performance for various image characteristics. We demonstrate that WindSTORM improves the computation speed by two orders of magnitude on CPU and three orders of magnitude upon GPU acceleration to realize online image processing, without compromising localization accuracy. Further, due to the embedded background correction, WindSTORM is highly robust in the presence of high and non-uniform background. WindSTORM paves the way for next generation of high-throughput nanoscopy.

scholarly journals An Improved Image Filtering Algorithm for Mixed Noise

2021 ◽  
Vol 11 (21) ◽  
pp. 10358
Author(s):  
Chun He ◽  
Ke Guo ◽  
Huayue Chen
Keyword(s):  
Gaussian Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Block Matching ◽  
Threshold Function ◽  
Median Filtering ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Mixed Noise ◽  
Salt And Pepper

In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.

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