Minimum Resolvable Contrast Research Articles

Maximum Sampling Region Acquisition and Contrast Calculation of Intensified Charge-Coupled Device Rectangular Target Image Under Low Illumination

Accurate acquisition of image contrast is an important factor for measuring modulation transfer function and minimum resolvable contrast of camera. Under low illumination, image from intensified charge-coupled device exhibits ion feedback noise with high brightness, resulting in lower signal-to-noise ratio of the image. Thus, one cannot obtain easily the accurate image contrast results by using the conventional contrast calculation method. In this paper, the ion feedback noise of the rectangular target image under low illumination is first reduced by means of constrained noise screening method and asymmetrical orientation window weighted mean filling algorithm. According to the spatial height correlation of the rectangular target image, the maximum sampling region is then founded by using spatial one-way statistical average algorithm combined with Otsu binarization. The image contrast is finally calculated after removing the fuzzy interval in the boundary between black and white stripes. It is shown that the method proposed can achieve the maximum sampling region acquisition and the contrast calculation for the image captured under low illumination, which reduces the error of calculation contrast caused by manually selecting the sampling region.

Image Chain Simulation for Earth Observation Satellites

We present a general-purpose end-to-end image chain simulation (ICS) that enables to assess the image quality of a satellite imager for Earth observations. The image chain consists of four main components: radiometry, atmosphere, optics, and detector. In particular, ICS first computes the input radiance from the reflectance values of a high-resolution input, and then calculates the image radiance by using the optical transfer function (OTF) of the overall system. This OTF contains all the distortion effects due to atmosphere, optics, and detector. Finally, the signal on the detector, including the noise term, is computed and converted to digital counts. To evaluate the overall image quality, metrics such as peak signal-to-noise ratio and minimum resolvable contrast are used. To illustrate the utility and versatility of the developed ICS, several analyses are also performed that demonstrate the system performance of a generic satellite imagery. Our development provides a unified framework for the ICS developers of spaceborne Earth-observing systems. Such a complete end-to-end ICS is crucial for the effective development of an Earth observation satellite, especially in the design and test phases.

低照度成像组件的最小可分辨率对比度测量及其作用距离模拟

低照度成像组件的最小可分辨率对比度测量及其作用距离模拟

Measurement of Minimum Resolvable Contrast Based on BP Neural Network

Aiming at the MTF evaluation limitations in visible imaging system, this paper introduces the minimum resolvable contrast. On the basis of MRC theoretical, the paper obtains subjective and objective methods of MRC measurement. Then MRC measurement based on neural network is put forward, which does not depend on subjective judgment of person. BP neural network is established and trained. Therefore, the network can replace human eyes to judge test patterns with different spatial frequencies and contrasts. Sony camera with 500 megapixels is selected in the experiment. Results show that MRC values of the objective measurement at all frequencies are less than those of the subjective measurement. The MRC Measurement has good stability.

Research on Minimum Resolvable Contrast of the Telescope

At present, the research on the minimum resolvable contrast is limited to the simple imaging system, or the imaging devices, and mainly limited to the method of measurement in the laboratory, the research on minimum resolvable contrast of the telescope has not developed. Based on the imaging process of the telescope and the detecting SNR, noise equivalent contrast is put forward, then combined with the system MTF, the model on minimum resolvable contrast of the telescope is build. At last, the model is simulated on the computer, the simulation results indicated that the minimum resolvable contrast can evaluate the telescope ability more comprehensive, the atmosphere, the aberration and the motion can affect the ability seriously. The model can give a scientific basis for the optimization design of the system and the development of practical work.

基于最小可分辨对比度的近红外距离选通成像系统作用距离模型

基于最小可分辨对比度的近红外距离选通成像系统作用距离模型

基于最小可分辨对比度的CCD成像系统最佳角放大率研究

基于最小可分辨对比度的CCD成像系统最佳角放大率研究

A new method to evaluate imaging quality of CCD cameras

In order to evaluate the imaging quality of CCD cameras fully and rapidly, the minimum resolvable contrast (MRC) is presented in this paper and the system of measuring MRC is constructed as well, in which two integrating spheres are proposed to illuminate two sides of the garget respectively. The variable contrast can be obtained by regulating the luminance of integrating spheres. Experimental results indicate that the error of measuring luminance is within ±0.3 cd/m2, MRC rises with the increase of the spatial frequency. The experimental results show that the method proposed is an effective approach to evaluate the imaging quality of CCD cameras.

A flat-panel detector based micro-CT system: performance evaluation for small-animal imaging

A dedicated small-animal x-ray micro computed tomography (micro-CT) system has been developed to screen laboratory small animals such as mice and rats. The micro-CT system consists of an indirect-detection flat-panel x-ray detector with a field-of-view of 120 × 120 mm2, a microfocus x-ray source, a rotational subject holder and a parallel data processing system. The flat-panel detector is based on a matrix-addressed photodiode array fabricated by a CMOS (complementary metal-oxide semiconductor) process coupled to a CsI:Tl (thallium-doped caesium iodide) scintillator as an x-ray-to-light converter. Principal imaging performances of the micro-CT system have been evaluated in terms of image uniformity, voxel noise and spatial resolution. It has been found that the image non-uniformity mainly comes from the structural non-uniform sensitivity pattern of the flat-panel detector and the voxel noise is about 48 CT numbers at the voxel size of 100 × 100 × 200 µm3 and the air kerma of 286 mGy. When the magnification ratio is 2, the spatial resolution of the micro-CT system is about 14 lp/mm (line pairs per millimetre) that is almost determined by the flat-panel detector showing about 7 lp/mm resolving power. Through low-contrast phantom imaging studies, the minimum resolvable contrast has been found to be less than 36 CT numbers at the air kerma of 95 mGy. Some laboratory rat imaging results are presented.

A photomultiplier-based secondary electron imaging system for a nuclear microprobe

The ability to define, or recognise particular regions of interest or surface features is vital to the analysis and interpretation of spatially-resolved images collected with a nuclear microprobe. However, good topographic image contrast is difficult to accomplish using PIXE or RBS images due to their inherent insensitivity to topography, lack of elemental variation or poor statistics. Topographic image contrast is commonly obtained in scanning electron microscopy (SEM) by detecting a large flux of secondary electrons produced by the focused keV electron beam. Similar systems have not been widely used on nuclear microprobes due to ion beam intensity fluctuations, which limit the minimum resolvable contrast and present a major limitation for this technique. This paper describes a secondary electron imaging system which has been developed on the Lisbon microprobe. It is based on a scintillator, a photomultiplier operated in a pulsed mode, a pulse shaping electronic chain and ADC, and requires no changes to the existing data acquisition system. Examples of the images obtained from materials such as patterned SiGe wafers and hydrogen-implanted silicon are given, and compared with SEM or optical images.

Target identification performance as a function of low spatial frequency image content

Current imaging system performance models use either the minimum resolvable temperature difference or the minimum resolvable contrast concepts to predict target identification performance. Both of these performance functions describe the limiting frequency that can be viewed through the imaging system at a particular contrast. No credit is given to the system for the amount of low frequency (lower than the limiting frequency) information that is passed through the system. We determine whether the low spatial frequency information is important in the target identification task. Previous experiments show that no degradation is seen on character recognition if a high-pass, edge enhancing filter is applied to character images. This is not the case in target identification performance, where the targets of interest are military tanks. A number of filters (six levels of blur at four bandwidth configurations) are applied to tank imagery including high-pass filters to reduce the low frequency image content. A perception experiment is performed to determine whether target identification performance was degraded with a reduced amount of low spatial frequency image content. The probability of target identification is calculated from the observer responses and the identification performance is evaluated as a function of low spatial frequency image content. Low frequency information is shown to contribute to the overall system performance.

Bias-free procedure for the measurement of the minimum resolvable temperature difference and minimum resolvable contrast

MRTD (Minimum Resolvable Temperature Difference) The characterization of electro-optical system performance by means of the standard minimum resolvable temperature difference (MRTD) or the minimum resolvable contrast (MRC) has a number of serious disadvantages. One of the problems is that they depend on the subjective decision criterion of the observer. We present an improved measurement procedure in which the results are free from observer bias. In an adaptive two-alternative forced-choice (2AFC) procedure, both the standard four-bar pattern and a five-bar reference pattern of the same size and contrast are presented consecutively in random order. The observer decides which of the two presentations contains the four-bar pattern. Misjudgments are made if the bars cannot be resolved or are distorted by sampling. The procedure converges to the contrast at which 75% of the observer responses are correct. The reliability of the responses is tested statistically. Curves cut off near the Nyquist frequency, so that it is not necessary to artificially set a frequency limit for sampling array cameras. The procedure enables better and easier measurement, yields more stable results than the standard procedure, and avoids disputes between different measuring teams. The presented procedure is a 'quick fix' solution for some of the problems with the MRTD and MRC, and is recommended as long as bar patterns are used as the stimulus. A new and fundamentally better method to characterize electro-optical system performance, called the triangle orientation discrimination threshold (TOD) was recently proposed by Bijl and Valeton (1998).

Validation of the new triangle orientation discrimination method and ACQUIRE model predictions using observer performance data for ship targets

In an extensive observer experiment, identification and classification performance with a CCD camera system was measured for ship targets. The experiment was specifically designed for target acquisition (TA) model validation and improvement. Target type, target aspect angle, viewing depression angle, target contrast and range were varied systematically, and 50% correct acquisition ranges were determined. The observer data were used to validate several basic assumptions in the TA model ACQUIRE and to test whether the recently developed triangle orientation discrimination (TOD) system performance characterization curve (Bijl and Valeton, 1998), when implemented in ACQUIRE, leads to performance predictions similar to or better than the minimum resolvable contrast (MRC) does. The results of the study are (1) the TOD curve predicts the effect of target contrast very well while the MRC curve is too steep, thus proving the value of characterizing a sensor with the new procedure; (2) ACQUIRE predicts the effect of target orientation on acquisition range quite well, using the square-root area of the target as characteristic size; (3) the Johnson cycle criteria for identification and classification of sea targets are 9 and 4, respectively, which means that angular dimensions for sea targets must be 1.5 to 2 times larger than for ground targets to obtain the same level of acquisition performance; (4) similar TOD acquisition criteria were defined, and these appeared to be quantitatively the same as the Johnson criteria; and (5) in 95% of the conditions, the ratio between predicted and measured range falls between 0.5 and 2.0 when the MRC is used, and between 0.6 and 1.6 when the TOD is used. Overall, considering the simplicity of ACQUIRE, its predictions are reasonable, especially when the TOD is used.

Triangle orientation discrimination: the alternative to minimum resolvable temperature difference and minimum resolvable contrast

Een nieuwe methode voor het karakteriseren van prestaties met electro- optische waarnemingsapparatuur wordt voorgesteld. De methodiek heeft een groot aantal voordelen boven de huidige NATO standaarden, de MRTD en de MRC, en voorspelt bovendien voor vele doelen veel beter.

The effect of temporal image filters on observer-dependent contrast metrics

Interframe correlation resulting from filtering diminishes eye integration signal-to-noise ratio (SNR) improvement. This reduces the SNR improvement used to predict improved minimum resolvable contrast or temperature difference to approximately half of that achieved at the monitor. Results are included for mean and recursive filters versus filter, delay, and blurring parameters.

Target Acquisition Criteria for Human Observers

A primary target acquisition threat to ground vehicle systems is the human observer whose psycho-visual capabilities are a critical element in the simulation of these phenomena. This paper discusses the Army's performance assessment methodology for determining signature countermeasures tradeoffs and the human factors elements essential for this type of analysis. Current Army acquisition models use a very simplistic model of the human visual perception system. The minimum resolvable contrast (MRC) models, for example, consider the target as an equivalent rectangle in a uniform background scene. The criteria for detection, recognition and identification are defined in terms of the number of line pairs across a critical target dimension. Although this simple model is convenient for many battlefield effectiveness simulations; the level of detail is insufficient for most CM applications. Several examples will illustrate the limitations of current target acquisition methodology for visual signature assessment and evaluation. A detailed description of the target/background scene and the observer psychovisual characteristics is essential for a robust simulation of these processes. In particular color, motion, shape, texture, search and clutter are essential parameters in any CM performance assessment methodology. The inclusion of these elements into future target acquisition models is the focal point for this discussion.