Abstract
Accurate measurement of image-sensor frequency response over a wide range of spatial frequencies is very important for analyzing pixel array characteristics, such as modulation transfer function (MTF), crosstalk, and active pixel shape. Such analysis is especially significant in computational photography for the purposes of deconvolution, multi-image superresolution, and improved light-field capture. We use a lensless interferometric setup that produces high-quality fringes for measuring MTF over a wide range of frequencies (here, 37 to 434 line pairs per mm). We discuss the theoretical framework, involving Michelson and Fourier contrast measurement of the MTF, addressing phase alignment problems using a moiré pattern. We solidify the definition of Fourier contrast mathematically and compare it to Michelson contrast. Our interferometric measurement method shows high detail in the MTF, especially at high frequencies (above Nyquist frequency). We are able to estimate active pixel size and pixel pitch from measurements. We compare both simulation and experimental MTF results to a lens-free slanted-edge implementation using commercial software.
Highlights
Sensor modulation transfer function (MTF) is generally defined as the spatial frequency response of the image sensor in the absence of optics
The MTF curves would be modulated by the Fourier fingerprint of the pixel shape, crosstalk “shape” and strength, and effects of noise
Computational photography is grounded in the image sensor
Summary
Refinements of traditional film and digital photography, such as light-field capture,[1] superresolution,[2] high dynamic range, etc., were once confined to professional photographers and optics researchers. Image sensors are a critical part of digital cameras and computational photography in general They are usually characterized by signal-to-noise ratio (SNR), wavelength response, and dynamic range. As sensor pixels shrink, evaluating the sensor MTF using common test chart-based methods (as defined in ISO 12233) may exhibit limitations on measurability or accuracy at high frequencies. This can happen due to limitations of the optics that create the image on the sensor. We utilize the detailed mathematics describing sensor response by considering the effects of discretization and spectral leakage (Sec. 3.2.3) and explicitly derive Fourier contrast in terms of Dirichlet kernels7 [Eq (22)]
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