- 标题
- 摘要
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- 实验方案
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Rome (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Bayesian Restoration of High-Dimensional Photon-Starved Images
摘要： This paper investigates different algorithms to perform image restoration from single-photon measurements corrupted with Poisson noise. The restoration problem is formulated in a Bayesian framework and several state-of-the-art Monte Carlo samplers are considered to estimate the unknown image and quantify its uncertainty. The different samplers are compared through a series of experiments conducted with synthetic images. The results demonstrate the scaling properties of the proposed samplers as the dimensionality of the problem increases and the number of photons decreases. Moreover, our experiments show that for a certain photon budget (i.e., acquisition time of the imaging device), downsampling the observations can yield better reconstruction results.
关键词： Bayesian statistics,Markov chain Monte Carlo,Inverse problems,Image processing,Bouncy particle sampler,Poisson noise
更新于2025-09-23 15:22:29
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An FPGA-Oriented Algorithm for Real-Time Filtering of Poisson Noise in Video Streams, with Application to X-Ray Fluoroscopy
摘要： In this paper we propose a new algorithm for real-time ?ltering of video sequences corrupted by Poisson noise. The algorithm provides effective denoising (in some cases overcoming the ?ltering performances of state-of-the-art techniques), is ideally suited for hardware implementation, and can be implemented on a small ?eld-programmable gate array using limited hardware resources. The paper describes the proposed algorithm, using X-ray ?uoroscopy as a case study. We use IIR ?lters for time ?ltering, which largely simpli?es hardware cost with respect to previous FIR ?lter-based implementations. A conditional reset is implemented in the IIR ?lter, to minimize motion blur, with the help of an adaptive thresholding approach. Spatial ?ltering performs a conditional mean to further reduce noise and to remove isolated noisy pixels. IIR ?lter hardware implementation is optimized by using a novel technique, based on Steiglitz–McBride iterative method, to calculate ?xed-point ?lter coef?cients with minimal number of nonzero elements. Implementation results using the smallest StratixIV FPGA show that the system uses only, at most, the 22% of the resources of the device, while performing real-time ?ltering of 1024 × 1024@49fps video stream. For comparison, a previous FIR ?lter-based implementation, on the same FPGA, in the same conditions and constraints (1024 × 1024@49fps), requires the 80% of the logic resources of the FPGA.
关键词： Poisson noise,X-ray video?uoroscopy processing,Field-programmable gate array (FPGA),IIR ?ltering,IIR ?lter design,Real-time video ?ltering
更新于2025-09-23 15:22:29
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Development of a defect recognition algorithm for visual laser-induced damage detection
摘要： Laser-induced damage is defined as a permanent detrimental change in the characteristics of an optical element caused by a laser beam. This change can be observed by many different inspection techniques, of which optical and phase imaging microscopic techniques have superior sensitivity. However, such examinations conducted by human operators are relatively slow and subjective—so they cannot be used for online damage monitoring purposes, whereas automatic inspection systems have advantages in terms of sensitivity, reliability, and speed. In this paper we introduce a new method for the computer-aided recognition of damaged sites based on visual images taken from the sample surface by a CCD camera. The evaluation procedure is performed by a computer algorithm, which consists of exact, statistically established steps. It includes noise reduction by considering the statistical behavior of photon noise. Besides, it takes into account the spatial extent of a damage spot by nonlinear image filtering to separate damage-indicating intensity changes from random noise. This mimics the ability of the human eye to distinguish features from their surroundings. The evaluation algorithm is built of computationally less demanding mathematical operations to enable fast execution which is vital for monitoring at high repetition rates. The proposed method was tested on a sizeable dataset of images yielding 98.8% of damage detection efficiency. It was also compared to a generally used visual laser damage detection procedure, which has a success rate of 88.6%. This yields one order of magnitude reduction in the number of undetected damaged sites.
关键词： image filter,Chebyshev’s theorem,laser-induced damage,machine vision,poisson noise
更新于2025-09-23 15:21:01
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Total generalized variation and shearlet transform based Poissonian image deconvolution
摘要： Integrating the advantages of total generalized variation and shearlet transform, this article introduces a hybrid regularizers scheme for deconvolving Poissonian image. Computationally, a highly efficient alternating minimization algorithm associated with variable splitting approach is described to obtain the optimal solution in detail. Illustrationally, in comparison with several current state-of-the-art numerical methods, numerical simulations consistently demonstrate the outstanding performance of our proposed approach to deblurring Poissonian image, in terms of both restoration accuracy and feature-preserving ability.
关键词： Image deconvolution,Alternating minimization method,Shearlet transform,Total generalized variation,Poisson noise
更新于2025-09-19 17:15:36
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[IEEE 2019 11th International Conference on Knowledge and Systems Engineering (KSE) - Da Nang, Vietnam (2019.10.24-2019.10.26)] 2019 11th International Conference on Knowledge and Systems Engineering (KSE) - A Fast Denoising Algorithm for X-Ray Images with Variance Stabilizing Transform
摘要： We propose a fast denoising algorithm for X-Ray images with variance stabilizing transformations. The variance stabilizing transformations are used to transform Poisson noisy images to Gaussian noisy images. Therefore, we can utilize advantages of the fast denoising algorithm based on the alternative direction method of multipliers. In experiments, we evaluate denoising quality by the Peak signal-to-noise ratio and the Structure Similarity metrics. Comparing results show that our method outperforms other similar denoising methods.
关键词： ROF model,Variance Stabilizing Transformations,Image Denoising,Poisson Noise,Medical Image Processing
更新于2025-09-12 10:27:22
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[IEEE 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) - Lviv, Ukraine (2018.9.11-2018.9.14)] 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) - The Methods for Evaluating the Quality of Images with Different Types of Noise
摘要： In the context of evaluating the quality of image recognition systems, it's worth noting that noise is not the only type of interference. Performing such actions on the image as, for example, purposeful modification, rotation or zooming of the image will also have a negative effect on the image resolution. It has been established that one of the reasons for the complication of the decision-making process is the deterioration of the quality of the input information obtained on the basis of various images due to overlaying noise on them, which may have different origin and characteristics. Studying a certain class of noise in the context of considering it as a function allows you to focus on determining its parameters, the degree of influence of these parameters and the artificial noise generation. An overview of the noise of different types and their effects was performed for further evaluation of recognition systems. Noises that arise in this case, are subject to classification in order to study, formalize and further eliminate or minimize their harmful effects. Studying a certain class of noise in the context of considering it as a function allows you to focus on determining its parameters, the degree of influence of these parameters and the artificial noise generation.
关键词： recognition systems quality evaluation,speckle noise,Perlin noise,Gaussian noise,Poisson noise,noise overlaying methods,photographic film grains noise
更新于2025-09-11 14:15:04
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Bayesian 3D Reconstruction of Subsampled Multispectral Single-photon Lidar Signals
摘要： Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high range precision and low laser power. A multispectral single-photon Lidar system provides additional spectral diversity, allowing the discrimination of different materials. However, the main drawback of such systems can be the long acquisition time needed to collect enough photons in each spectral band. In this work, we tackle this problem in two ways: first, we propose a Bayesian 3D reconstruction algorithm that is able to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions. In contrast to previous algorithms, the novel method processes jointly all the spectral bands, obtaining better reconstructions using less photon detections. The proposed model promotes spatial correlation between neighbouring points within a given surface using spatial point processes. Secondly, we account for different spatial and spectral subsampling schemes, which reduce the total number of measurements, without significant degradation of the reconstruction performance. In this way, the total acquisition time, memory requirements and computational time can be significantly reduced. The experiments performed using both synthetic and real single-photon Lidar data demonstrate the advantages of tailored sampling schemes over random alternatives. Furthermore, the proposed algorithm yields better estimates than other existing methods for multi-surface reconstruction using multispectral Lidar data.
关键词： Poisson noise,Bayesian inference,multispectral imaging,Lidar,3D reconstruction,Markov chain Monte Carlo
更新于2025-09-11 14:15:04
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Point Spread Functions in Identification of Astronomical Objects from Poisson Noised Image
摘要： This article deals with modeling of astronomical objects, which is one of the most fundamental topics in astronomical science. Introduction part is focused on problem description and used methods. Point Spread Function Modeling part deals with description of basic models used in astronomical photometry and further on introduction of more sophisticated models such as combinations of interference, turbulence, focusing, etc. This paper also contains a way of objective function definition based on the knowledge of Poisson distributed noise, which is included in astronomical data. The proposed methods are further applied to real astronomical data.
关键词： point spread function,objective function,Poisson noise,Astronomical image
更新于2025-09-09 09:28:46