A method of calibration of imaging luminance measuring devices has been studied. By the device-independent color space transformation, the color image by digital camera could be converted to the CIE's absolute color space lab. Then, the calibration model is fitted between ln(L/t) and luminance. At last, luminance image is obtained and the dynamic range of luminance image could be adjusted by shutter speed.
In this paper, the compact spectrometer has been designed and implemented with concave grating. By using the holographic corrected concave grating, the compact spectrometer without movable parts, with a fixed grating and an array detector, could obtain a relative high spectral resolution in a wide spectral range. Then, the spectral resolution has been estimated by the slit function. The spectral resolution (Δ<sub>FWHM</sub>) is smaller than 5nm from 300nm to 1100nm. It is very suitable for photometry, colorimetry, and radiometry.
Aiming at the problems of temperature measurement and the defects of radiance thermometry theory, one method of spectral-based inferential measurement is proposed, which adopts the Empirical Risk Minimization (ERM) functional model as the temperature measurement model. Then, the radiance thermometry theory and inferential measurement technology are discussed comparatively. Temperatures of some targets, such and tungsten lamp and solar surface, are measured by spectral-based inferential measurement.
In the view of the situation that single-sensor image cannot fully reflect the scene information efficiently. A fusion method of infrared and visible images based on discrete wavelet transform is presented and comparatively analyzed with traditional methods. Firstly, the wavelet multi-scale decomposition technique is applied to the source images that will be fused to give a series of sub-band coefficient. Feature extraction and weighted average with adaptive weighting factors are used to process the high-frequency coefficients. A strategy of the absolute value comparing is adopted to the low-frequency coefficients. Finally, the fusion image is reconstructed by multi-scale wavelet inversing transformation for low frequency and high frequency coefficients. Experimental results demonstrate that infrared and visible images can be more effectively fused by the algorithm presented than traditional methods.
Proc. SPIE. 9795, Selected Papers of the Photoelectronic Technology Committee Conferences held June–July 2015
KEYWORDS: Signal to noise ratio, Modulation, Imaging systems, Spatial frequencies, Image quality, Data processing, Charge-coupled devices, Modulation transfer functions, Data conversion, CCD image sensors
The Modulation Transfer Function (MTF) is a fundamental imaging system design specification and system quality metric often used in remote sensing. The MTF describes the attenuation of sinusoidal waveforms as a function of spatial frequency. Practically, MTF is a metric quantifying the sharpness of the reconstructed image. The Knife-Edge method is becoming widely applied for its advantage of simplified target and accurate computer calculation. Noise in CCD image system is inevitable, thus the SNR becomes a factor influencing the MTF measurement. In this paper, we build relationships between SNR, luminance and MTF. In conclusion, SNR is related with luminance levels linearly. SNR rises with increasing luminance. The higher SNR, the more curves conform to the theoretical MTF.