CN105865748B - A kind of test method of the imaging sensor critical performance parameters based on particle cluster algorithm - Google Patents
A kind of test method of the imaging sensor critical performance parameters based on particle cluster algorithm Download PDFInfo
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- CN105865748B CN105865748B CN201610297894.4A CN201610297894A CN105865748B CN 105865748 B CN105865748 B CN 105865748B CN 201610297894 A CN201610297894 A CN 201610297894A CN 105865748 B CN105865748 B CN 105865748B
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Abstract
The invention belongs to field of photoelectric technology, are specially related to a kind of test method of the imaging sensor critical performance parameters based on particle cluster algorithm.It specifically includes：Operation is exposed to imaging sensor, n different time for exposure is taken, then acquire n frame light field data and n frame details in a play not acted out on stage, but told through dialogues data respectively, is required according to EMVAStandard1288, calculate light field value varianceLight field gray value mean μy, details in a play not acted out on stage, but told through dialogues gray value μydark；Carry out the specific steps of particle cluster algorithm inverting.The present invention can quickly and conveniently obtain system gain and dark noise parameter.
Description
Technical Field
The invention belongs to the technical field of photoelectricity, and particularly relates to a particle swarm algorithm-based method for testing key performance parameters of an image sensor.
Background
The development speed of the image sensor is fast, so that the image sensor is already applied to various aspects of people's social life. From applications such as digital cameras, smart phones and cameras for security monitoring which are most close to life, to novel weapons closely related to national defense security, and even the space science which is the highest embodiment of human science and technology development, the body shadow of the image sensor is available. In the fields of production and important applications, the performance of sensor chips is required to be tested, evaluated and screened in order to monitor the product quality or to ensure the reliability of the application. Among the measured parameters of an image sensor, the most fundamental and important parameters are the system gain of the sensor and the dark field noise.
The particle swarm optimization algorithm is a new evolutionary algorithm developed in recent years, and is a global optimum which is found by starting from a random solution, iteratively finding an optimal solution, evaluating the solution by utilizing fitness to obtain quality, and following the optimal value searched currently. The algorithm draws attention from academic circles due to the advantages of easy implementation, high precision, fast convergence and the like, and shows superiority in solving practical problems.
Disclosure of Invention
The invention aims to provide a particle swarm algorithm-based method for testing key performance parameters of an image sensor.
The purpose of the invention is realized as follows:
the method for testing the key performance parameters of the image sensor based on the particle swarm optimization comprises the following steps:
(1) exposing the image sensor, taking n different exposure times, respectively acquiring n frames of bright field data and n frames of dark field data, and calculating the variance of the bright field values according to the requirements of an EMVAStandard1288Mean value of the grey values μ of the bright fieldyDark field gray value μydark；
(2) The particle swarm algorithm inversion method comprises the following specific steps:
(2.1) System gain K, dark Current μ of image sensorIInitial darknessVariance of signalComposing three-dimensional particles;
(2.2) setting the individual fitness function of the particles as follows:
wherein,is the variance of the grey value of the bright field, K is the system gain, μd.0Is the initial value of the dark signal, muIIs dark current, texpAs exposure time, σqIs quantization noise;
and (2.3) obtaining the optimal estimated values of system gain, dark current and initial dark signal variance according to the optimization flow of the particle swarm optimization.
The invention has the beneficial effects that:
the invention can more conveniently obtain the system gain and the dark noise parameters.
Drawings
FIG. 1 is a schematic diagram of the steps of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention discloses a particle swarm optimization-based method for optimizing parameters of an image sensor, which comprises the steps of exposing the image sensor for multiple times under the conditions of a bright field and a dark field, and respectively calculating a bright field variance, a dark field variance and a dark field variance according to 'EMVAStandard 1288' (an image sensor and a camera test standard established by European machine vision Association),And applying the data and the corresponding exposure time data to a particle swarm algorithm. According to the photon conversion theory, a fitness function is designed, and the system gain K and the dark current mu of the image sensor are measuredIInitial dark signal varianceAnd forming three-dimensional particles, and obtaining an inversion result of system gain, dark current and initial dark signal variance after optimization by a particle swarm algorithm. The method is insensitive to dark field variance
The invention is based on a particle swarm optimization algorithm, carries out inversion on key parameters of an image sensor, and comprises the following steps:
s1: and carrying out exposure operation on the image sensor, taking n different exposure times, and respectively acquiring n frames of bright field data and n frames of dark field data. Calculating the variance of the bright field value according to the requirements of the EMVAStandard1288 standardMean value of the grey values μ of the bright fieldyDark field gray value μydark；
S2: the particle swarm algorithm inversion method comprises the following specific steps:
s2.1: system gain K and dark current mu of image sensorIInitial dark signal varianceComposing three-dimensional particles;
s2.2: setting the individual fitness function of the particles as follows:
wherein,is an brightVariance of field gray value, K is system gain, μd.0Is the initial value of the dark signal, muIIs dark current, texpAs exposure time, σqIs quantization noise;
s2.3: and obtaining the optimal estimated values of system gain, dark current and initial dark signal variance according to the optimization flow of the particle swarm optimization.
The invention designs a fitness function containing measurable data and measured parameters on the basis of an image sensor test standard EMVAStandard1288 (an image sensor and camera test standard established by European machine vision Association), and provides a novel image sensor key parameter test method by combining a particle swarm algorithm.
Firstly, according to the EMVAStandard1288 standard, exposure is carried out in a bright field and dark field mode under 30 different exposure times, and 30 groups of bright field gray value variance, bright field gray value mean value and dark field gray value mean value are obtained.
The total search times of the particle swarm optimization is 600, the system gain K and the dark current muIInitial dark signal varianceThree-dimensional particles were composed, and 400 particles were randomly generated as an initial population. After the particles are generated, setting a fitness function set in S3 in claim 1, calculating a fitness function value of each individual, judging whether a preset termination condition of the particle swarm algorithm is reached, if not, entering a step, updating the position and speed of the particles in the algorithm according to the fitness value in the particle swarm algorithm, and gradually evolving and searching until the termination condition of the algorithm is met, wherein the particles with the highest fitness value are the inversion results of the system gain, the dark current and the initial dark signal variance of the image sensor.
Claims (1)
1. A method for testing key performance parameters of an image sensor based on a particle swarm algorithm is characterized by comprising the following steps:
(1) carrying out exposure operation on the image sensor, taking n different exposure times, respectively acquiring n frames of bright field data and n frames of dark field data, and calculating the variance of the gray value of the bright field according to the requirements of an EMVA Standard1288Mean value of the grey values μ of the bright fieldyDark field gray scaleValue of muydark；
(2) The particle swarm algorithm inversion method comprises the following specific steps:
(2.1) System gain K, dark Current μ of image sensorIInitial dark signal varianceComposing three-dimensional particles;
(2.2) setting the individual fitness function of the particles as follows:
wherein,is the variance of the grey value of the bright field, K is the system gain, μIIs dark current, texpAs exposure time, σqIs quantization noise;
and (2.3) obtaining the optimal estimated values of system gain, dark current and initial dark signal variance according to the optimization flow of the particle swarm optimization.
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Citations (2)
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| CN103698682A (en) * | 2013-12-20 | 2014-04-02 | 中国空间技术研究院 | CMOS (Complementary Metal-Oxide-Semiconductor Transistor) image sensor testing device on basis of FPGA (Field Programmable Gate Array) technology |
| CN103914831A (en) * | 2014-03-10 | 2014-07-09 | 西安电子科技大学 | Two-dimensional dual-threshold SAR image segmentation method based on quantum particle swarm optimization |
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| JP2010034703A (en) * | 2008-07-25 | 2010-02-12 | Fujifilm Corp | Method and equipment for inspecting image sensor |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103698682A (en) * | 2013-12-20 | 2014-04-02 | 中国空间技术研究院 | CMOS (Complementary Metal-Oxide-Semiconductor Transistor) image sensor testing device on basis of FPGA (Field Programmable Gate Array) technology |
| CN103914831A (en) * | 2014-03-10 | 2014-07-09 | 西安电子科技大学 | Two-dimensional dual-threshold SAR image segmentation method based on quantum particle swarm optimization |
Non-Patent Citations (2)
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| CCD光电参数测试系统的研制;周跃 等;《红外与激光工程》;20141031;第43卷(第10期);第3451-3456页 * |
| CCD芯片性能参数测量系统;许宏涛 等;《仪器仪表学报》;20110630;第32卷(第6期);第271-275页 * |
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