scholarly journals Automated Calibration Method for Eye-Tracked Autostereoscopic Display

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2614
Author(s):  
Hyoseok Hwang
Keyword(s):  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Structural Similarity ◽  
Calibration Method ◽  
Qualitative Assessment ◽  
Eye Position ◽  
Calibration System ◽  
Calibration Process ◽  
Autostereoscopic Display ◽  
Automated Calibration

In this paper, we propose an automated calibration system for an eye-tracked autostereoscopic display (ETAD). Instead of calibrating each device sequentially and individually, our method calibrates all parameters of the devices at the same time in a fixed environment. To achieve this, we first identify and classify all parameters by establishing a physical model of the ETAD and describe a rendering method based on a viewer’s eye position. Then, we propose a calibration method that estimates all parameters at the same time using two images. To automate the proposed method, we use a calibration module of our own design. Consequently, the calibration process is performed by analyzing two images captured by onboard camera of the ETAD and the external camera of the calibration module. For validation, we conducted two types of experiments, one with simulation for quantitative evaluation, and the other with a real prototype ETAD device for qualitative assessment. Experimental results demonstrate the crosstalk of the ETAD was improved to 8.32%. The visual quality was also improved to 30.44% in the peak-signal-to-noise ratio (PSNR) and 40.14% in the structural similarity (SSIM) indexes when the proposed calibration method is applied. The whole calibration process was carried out within 1.5 s without any external manipulation.

scholarly journals Comparative analysis of different image denoising methods in spatial and transform domain

2021 ◽  
Author(s):  
Zeeshan Ahmad
Keyword(s):  
Comparative Analysis ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Structural Similarity ◽  
Noise Model ◽  
Transform Domain ◽  
Noise Levels ◽  
Extract Information

Digital Images are the best source for humans to see, visualize, think, extract information and make conclusions. However during the acquisition of images, noise superimposes on the images and reduces the information and detail of the images. In order to restore the details of the images, noise must be reduced from the images. This requirement places the image denoising amongst the fundamental and challenging fields of computer vision and image processing. In this project six fundamental techniques / algorithms of image denoising in spatial and transform domain are presented and their comparative analysis is also carried out. The noise model used in this project is Additive Gaussian noise. The algorithms are simulated on Matlab and experimental results are shown at different noise levels. The performance of each image denoising technique is measured in terms of Peak Signal to Noise Ratio (PSNR) , Mean Structural Similarity (SSIM) Metrics and visual quality. It is observed that the transform domain techniques used in this project achieved better results as compared to spatial domain techniques

scholarly journals Comparative analysis of different image denoising methods in spatial and transform domain

2021 ◽  
Author(s):  
Zeeshan Ahmad
Keyword(s):  
Comparative Analysis ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Structural Similarity ◽  
Noise Model ◽  
Transform Domain ◽  
Noise Levels ◽  
Extract Information

Digital Images are the best source for humans to see, visualize, think, extract information and make conclusions. However during the acquisition of images, noise superimposes on the images and reduces the information and detail of the images. In order to restore the details of the images, noise must be reduced from the images. This requirement places the image denoising amongst the fundamental and challenging fields of computer vision and image processing. In this project six fundamental techniques / algorithms of image denoising in spatial and transform domain are presented and their comparative analysis is also carried out. The noise model used in this project is Additive Gaussian noise. The algorithms are simulated on Matlab and experimental results are shown at different noise levels. The performance of each image denoising technique is measured in terms of Peak Signal to Noise Ratio (PSNR) , Mean Structural Similarity (SSIM) Metrics and visual quality. It is observed that the transform domain techniques used in this project achieved better results as compared to spatial domain techniques

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

A novel and sensitive LC-MS/MS method for the quantitation of ceftiofur in pharmaceutical preparations and milk samples

2020 ◽  
Vol 23 ◽  
Author(s):  
Serkan Levent ◽  
Saniye Özcan ◽  
Aysun Geven ◽  
Nafiz Öncü Can
Keyword(s):  
Quantitative Estimation ◽  
Signal To Noise Ratio ◽  
Multiple Reaction Monitoring ◽  
Pharmaceutical Preparations ◽  
Calibration Method ◽  
Negative Ion ◽  
Cow Milk ◽  
Tandem Mass ◽  
Ionization Source ◽  
Liquid Chromatography Tandem Mass

Introduction:: In the present study, a sensitive and selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was described for the determination of ceftiofur (CEF) in cow milk and pharmaceutical preparations. CEF is an antibiotic compound, which is commonly used in the treatment of animal diseases such as respiratory system, soft tissue, and foot infections, as well as postpartum acute puerperal metritis. One of the critical features of CEF is its prescription while breastfeeding of cows; in accordance, its quantitative estimation is essential to assess its residual amounts. Methods:: In the method reported herein, after simple protein precipitation using acetonitrile, the pre-treated samples were introduced in to an LC-MS/MS instrument equipped with a Chromolith® High-Resolution RP-18 series HPLC column (100 mm × 4.6 mm from Merck KGaA, Germany). Electrospray ionization was employed as the ionization source in the triplequadrupole tandem mass spectrometer. Results:: For the calibration method using solvent-based standards; LOQ was 3.038 ng/mL, 12.15 ng/mL, and LOD was 1.215 ng/mL and 6.076 ng/mL for ESI+ and ESI- modes, respectively. On the other hand, for the method of matrix-matched standards; LOQ was 1.701 ng/mL, 10.13 ng/mL, and LOD was 0.486 ng/mL and 5.929 ng/mL for ESI+ and ESI- modes, respectively as obtained from signal to noise ratio. Conclusion:: Applicability of both positive and negative ion modes was tested, and the analyte was detected via multiple reaction monitoring. The distorting effects of the milk matrix on the MS ionization and quantitation of CEF were overcome by using matrix-matched calibration for the first time.

scholarly journals Computing 3D Phase-Type Holograms Based on Deep Learning Method

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 280
Author(s):  
Huadong Zheng ◽  
Jianbin Hu ◽  
Chaojun Zhou ◽  
Xiaoxi Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Contrast Ratio ◽  
Angular Spectrum ◽  
Structural Similarity ◽  
Computational Time ◽  
Experimental Investigations ◽  
Training Strategy ◽  
Holographic Display ◽  
Phase Type

Computer holography is a technology that use a mathematical model of optical holography to generate digital holograms. It has wide and promising applications in various areas, especially holographic display. However, traditional computational algorithms for generation of phase-type holograms based on iterative optimization have a built-in tradeoff between the calculating speed and accuracy, which severely limits the performance of computational holograms in advanced applications. Recently, several deep learning based computational methods for generating holograms have gained more and more attention. In this paper, a convolutional neural network for generation of multi-plane holograms and its training strategy is proposed using a multi-plane iterative angular spectrum algorithm (ASM). The well-trained network indicates an excellent ability to generate phase-only holograms for multi-plane input images and to reconstruct correct images in the corresponding depth plane. Numerical simulations and optical reconstructions show that the accuracy of this method is almost the same with traditional iterative methods but the computational time decreases dramatically. The result images show a high quality through analysis of the image performance indicators, e.g., peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and contrast ratio. Finally, the effectiveness of the proposed method is verified through experimental investigations.

Joint Encryption and Compression-Based Watermarking Technique for Security of Digital Documents

2021 ◽  
Vol 21 (1) ◽  
pp. 1-20
Author(s):  
A. K. Singh ◽  
S. Thakur ◽  
Alireza Jolfaei ◽  
Gautam Srivastava ◽  
MD. Elhoseny ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Digital Data ◽  
The Internet ◽  
Discrete Wavelet ◽  
Compression Performance ◽  
Strong Compression ◽  
Multiple Watermarks ◽  
Value Decomposition

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. To achieve this, in this article, we propose a joint encryption then-compression based watermarking technique for digital document security. This technique offers a tool for confidentiality, copyright protection, and strong compression performance of the system. The proposed method involves three major steps as follows: (1) embedding of multiple watermarks through non-sub-sampled contourlet transform, redundant discrete wavelet transform, and singular value decomposition; (2) encryption and compression via SHA-256 and Lempel Ziv Welch (LZW), respectively; and (3) extraction/recovery of multiple watermarks from the possibly distorted cover image. The performance estimations are carried out on various images at different attacks, and the efficiency of the system is determined in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and compression ratio (CR). Furthermore, the comparative analysis of the proposed system with similar schemes indicates its superiority to them.

Study on Intelligent Controller Design of Flow Metrological Calibration System

2020 ◽  
Author(s):  
Xiong Yin ◽  
Kai Wen ◽  
Yan Wu ◽  
Lei Zhou ◽  
Jing Gong
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Pid Controller ◽  
Controller Design ◽  
Calibration System ◽  
Calibration Process ◽  
Intelligent Controller ◽  
Flow Adjustment ◽  
Precise Adjustment ◽  
Valve Opening

Abstract In recent years, China ramped up imports of natural gas to satisfy the growing demand, which has increased the number of trade meters. Natural gas flowmeters need to be calibrated regularly at calibration stations to ensure their accuracy. Nowadays, the flow metrological calibration process is done by the operator manually in China, which is easy to be affected by personnel experience and proficiency. China is vigorously developing industry 4.0 and AI(artificial intelligence) technologies. In order to improve the calibration efficiency, a design scheme of intelligent controller for flow metrological calibration system is first proposed in this paper. The intelligent controller can replace the operator for process switching and flow adjustment. First, the controller selects the standard flowmeter according to the type of the calibrated flowmeter, and switches the calibration process. To accurately control the calibration flow for 180 seconds, the controller continuously adjusts the regulating valve with a sequence of commands to the actuator. These commands are generated by intelligent algorithm which is predefined in the controller. Process switching is operated automatically according to flowmeter calibration specifications. In order to reach the required flow point quickly, the flow adjustment is divided into two steps: preliminary adjustment and precise adjustment. For preliminary adjustment, a BP neural network will be built first using the field historical data and simulation results. This neural network describes the relationship between the valve-opening scheme and the calibration flow. Therefore, it could give a calibration flow as close as possible to the expected value during calibration. For precise adjustment, an adaptive PID controller is used. It could adjust the valve opening degree automatically to make sure the flow deviation meet the calibration requirements. Since the PID controller is a self-adaptive PID controller, the process of adjustment is very quick, which can reduce the calibration time largely. After each calibration, both the original neural network and the adaptive function of the controller will be updated to achieve the self-growth. With the information of the calibrated flowmeter, the entire calibration system can run automatically. The experiment in a calibration station shows that the intelligent controller can control the deviation of the flow value within 5% during 4∼5 minutes.

The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks

2021 ◽  
pp. 1-10
Author(s):  
Hongguang Pan ◽  
Fan Wen ◽  
Xiangdong Huang ◽  
Xinyu Lei ◽  
Xiaoling Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Super Resolution ◽  
Structural Similarity ◽  
Plug And Play ◽  
Attention Networks ◽  
Frequency Information ◽  
Blind Deblurring ◽  
Adaptive Adjustment ◽  
Deep Feature Extraction

In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

Sign in / Sign up

Export Citation Format

Share Document