A real-time video denoising algorithm with FPGA implementation for Poisson–Gaussian noise

Tan, Xin; Liu, Yu; Zuo, Chenglin; Zhang, Maojun

doi:10.1007/s11554-014-0405-2

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…In this context, an Additive White Gaussian Noise generator [31] has been developed to introduce random errors into the states vector. In particular, a single AWGN generator has been exploited for X and Y positions, yaw angle and velocity to insert possible external disturbances, coming from a real environment.…”

Section: Real-time Robust Mpc With Measurement Noise Injectionmentioning

confidence: 99%

Processor-in-the-Loop Validation of a Gradient Descent-Based Model Predictive Control for Assisted Driving and Obstacles Avoidance Applications

Dini

Saponara

2022

IEEE Access

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For safety-critical applications, the validation process using a model-based approach plays an increasingly important role. In this paper we propose the application of a predictive control algorithm, entirely implemented in low-level code, to the use case of assisted driving of four-wheel vehicles. The aim is to present the workflow for the validation of an advanced control algorithm and its implementation on an Embedded system, representative of the computational capabilities of Automotive ECUs. The proposed validation exploits the SIL (Software-In-the-Loop) and PIL (Processor-In-the-Loop) paradigms to analyse the combination of control parameters and factors related to the choice of the mathematical model describing the vehicle behaviour and the choice of the numerical algorithms selected to approximate the differential equations.

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Section: Real-time Robust Mpc With Measurement Noise Injectionmentioning

confidence: 99%

Processor-in-the-Loop Validation of a Gradient Descent-Based Model Predictive Control for Assisted Driving and Obstacles Avoidance Applications

Dini

Saponara

2022

IEEE Access

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“…• Adaptive grouping technique: Based on the spatial distance between the candidate patches that belong to the given reference patch, and the given reference patch, the adaptive grouping technique (AGT) is applied in this step. In the AGT technique, when the distance between the given reference patch and its candidate patches is sufficiently small, the AGT technique adopts the proposed dissimilarity mea- surement (13) in computing this distance. While the traditional dissimilarity measurement (12) and k-means clustering are adopted when the distance is large enough.…”

Section: • Adaptive Threshold Techniquementioning

confidence: 99%

“…The process of recording, capturing, and transmitting, predominantly involves a risk of video signal corruption. In many practical applications, video denoising is usually utilised to eliminate the noise interventions in the corrupted videos [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In recent decades, video noise reduction has been extensively studied in the literature, and many denoising schemes were proposed.…”

Section: Introductionmentioning

confidence: 99%

A novel video noise reduction method based on PDE, adaptive grouping, and thresholding techniques

Yahya

Tan

2021

J. eng.

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Undoubtedly, video block‐matching and 3D filtering (VBM3D) has achieved a significant improvement in video denoising. Nevertheless, in practice, failure to distinguish between the different noise areas, ignoring noise variances and pixel intensity, false‐similar patches, and poor matching are the challenges faced by the VBM3D filter. To avoid these drawbacks, a new video denoising algorithm is proposed. This algorithm based on the nature of the noise areas and the spatial distance between the reference block and its candidate blocks. In the algorithm, hard‐thresholding in VBM3D is replaced by adaptive filtering. In this adaptive filter, soft‐thresholding is applied to the heavily contaminated areas, whereas anisotropic diffusion filter is applied to the slight‐noise areas. Applying adaptive filtering creates a balance between noise removal and edges conservation. To avert the occurrence of the poor choice of the threshold, noise variances, clean image coefficients, and pixel intensity are taken into consideration during computing the proposed adaptive threshold. Due to the strong possibility of similar correlative blocks happening in the vicinity, an adaptive grouping technique is proposed to compute the distance between a reference block and its candidate blocks. Applying this technique helps to reduce the occurrence of false‐similar blocks and poor matching.

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“…[5] proposed a novel adaptive real-time video image enhancement based on a variational model of the Retinex theory; Tan et al [6] improved the denoising capability of pixel similarity weighted frame averaging (PSWFA); Anbarjafari et al [7] proposed a new video resolution enhancement technique using wavelet transform and an illumination compensation technique; Singh et al [8] introduced a novel framework for speckle noise removal and contrast enhancement of real ultrasound videos. These are some of the techniques used to improve the video quality.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric

Güngör

2021

Sakarya University Journal of Science

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Digital images obtained from the video frames have an important role in different areas. Many image processing techniques have been applied to digital images for different purposes such as edge detection. For a better image processing application, it is very important to obtain images with less oscillation from the video. However, the factors such as camera and environment cause differences among the consecutive frames. These differences cause images with oscillation. Statistical methods can be used to obtain images with less oscillation from multiple frames. In this paper, we developed a quality metric to compare the frames or images in accordance with the quantity of oscillation. A comparative study of statistical methods used to obtain the images with less oscillation from the video frames was presented. Images were obtained by using four statistical methods for the different numbers of frames. This study also focuses on evaluating how the statistical method choice affects the oscillation of images using the proposed quality metric and comparing the processing times of the methods.

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A real-time video denoising algorithm with FPGA implementation for Poisson–Gaussian noise

Cited by 13 publications

References 21 publications

Processor-in-the-Loop Validation of a Gradient Descent-Based Model Predictive Control for Assisted Driving and Obstacles Avoidance Applications

Processor-in-the-Loop Validation of a Gradient Descent-Based Model Predictive Control for Assisted Driving and Obstacles Avoidance Applications

A novel video noise reduction method based on PDE, adaptive grouping, and thresholding techniques

Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric

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