Information capacity of seeing limited imaging systems

2001 ◽  
Author(s):  
Sudhakar Prasad
Keyword(s):  
Imaging Systems ◽  
Information Capacity

Measuring camera Shannon Information Capacity with a Siemens Star Image

2020 ◽  
Vol 2020 (9) ◽  
pp. 347-1-347-10 ◽  
Author(s):  
Norman L. Koren
Keyword(s):  
Image Processing ◽  
Figure Of Merit ◽  
Imaging Systems ◽  
Shannon Information ◽  
Information Capacity ◽  
Exposure Index ◽  
Jpeg Images ◽  
Star Image ◽  
Performance Metric ◽  
Bilateral Filters

Shannon information capacity, which can be expressed as bits per pixel or megabits per image, is an excellent figure of merit for predicting camera performance for a variety of machine vision applications, including medical and automotive imaging systems. Its strength is that is combines the effects of sharpness (MTF) and noise, but it has not been widely adopted because it has been difficult to measure and has never been standardized. We have developed a method for conveniently measuring information capacity from images of the familiar sinusoidal Siemens Star chart. The key is that noise is measured in the presence of the image signal, rather than in a separate location where image processing may be different—a commonplace occurrence with bilateral filters. The method also enables measurement of SNRI, which is a key performance metric for object detection. Information capacity is strongly affected by sensor noise, lens quality, ISO speed (Exposure Index), and the demosaicing algorithm, which affects aliasing. Information capacity of in-camera JPEG images differs from corresponding TIFF images from raw files because of different demosaicing algorithms and nonuniform sharpening and noise reduction.

scholarly journals Spatial resolution, signal-to-noise and information capacity of linear imaging systems

2016 ◽  
Vol 24 (15) ◽  
pp. 17168 ◽  
Author(s):  
Timur Gureyev ◽  
Yakov Nesterets ◽  
Frank de Hoog
Keyword(s):  
Spatial Resolution ◽  
Imaging Systems ◽  
Information Capacity ◽  
Signal To Noise

Digital imaging and quantitative image analysis of polymer blends

1996 ◽  
Vol 54 ◽  
pp. 170-171
Author(s):  
Xiao Zhang
Keyword(s):  
Digital Imaging ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Polymer Science ◽  
Key Factors ◽  
Multiple Imaging ◽  
Quantitative Image ◽  
Digital Imaging Systems ◽  
Multi Phase ◽  
Network Technologies

Polymer microscopy involves multiple imaging techniques. Speed, simplicity, and productivity are key factors in running an industrial polymer microscopy lab. In polymer science, the morphology of a multi-phase blend is often the link between process and properties. The extent to which the researcher can quantify the morphology determines the strength of the link. To aid the polymer microscopist in these tasks, digital imaging systems are becoming more prevalent. Advances in computers, digital imaging hardware and software, and network technologies have made it possible to implement digital imaging systems in industrial microscopy labs.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Increasing the information capacity of a fiber-optic multi-sensor converter of binary mechanical signals into electrical signals

2020 ◽  
pp. 15-23
Author(s):  
V. M. Grechishnikov ◽  
E. G. Komarov
Keyword(s):  
Fiber Optic ◽  
Information Capacity ◽  
Parallel Structure ◽  
Optical Scheme ◽  
Digital To Analog Converter ◽  
Electrical Signals ◽  
Mechanical Signals ◽  
Analog Converter ◽  
Output Code ◽  
Digital To Analog

The design and operation principle of a multi-sensor Converter of binary mechanical signals into electrical signals based on a partitioned fiber-optic digital-to-analog Converter with a parallel structure is considered. The digital-to-analog Converter is made from a set of simple and technological (three to five digit) fiber-optic digital-to-analog sections. The advantages of the optical scheme of the proposed. Converter in terms of metrological and energy characteristics in comparison with single multi-bit converters are justified. It is shown that by increasing the number of digital-analog sections, it is possible to repeatedly increase the information capacity of a multi-sensor Converter without tightening the requirements for its manufacturing technology and element base. A mathematical model of the proposed Converter is developed that reflects the features of its operation in the mode of sequential time conversion of the input code vectors of individual fiber-optic sections into electrical analogues and the formation of the resulting output code vector.

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