Using polynomials to simplify fixed pattern noise and photometric correction of logarithmic CMOS image sensors.

An important class of complementary metal-oxide-semiconductor (CMOS) image sensors are those where pixel responses are monotonic nonlinear functions of light stimuli. This class includes various logarithmic architectures, which are easily capable of wide dynamic range imaging, at video rates, but which are vulnerable to image quality issues. To minimize fixed pattern noise (FPN) and maximize photometric accuracy, pixel responses must be calibrated and corrected due to mismatch and process variation during fabrication.

Digital pixel sensor array with logarithmic delta-sigma architecture.

Like the human eye, logarithmic image sensors achieve wide dynamic range easily at video rates, but, unlike the human eye, they suffer from low peak signal-to-noise-and-distortion ratios (PSNDRs). To improve the PSNDR, we propose integrating a delta-sigma analog-to-digital converter (ADC) in each pixel. An image sensor employing this architecture is designed, built and tested in 0.

Maximum Likelihood Estimation of Depth Maps Using Photometric Stereo.

Photometric stereo and depth-map estimation provide a way to construct a depth map from images of an object under one viewpoint but with varying illumination directions. While estimating surface normals using the Lambertian model of reflectance is well established, depth-map estimation is an ongoing field of research and dealing with image noise is an active topic. Using the zero-mean Gaussian model of image noise, this paper introduces a method for maximum likelihood depth-map estimation that accounts for the propagation of noise through all steps of the estimation process.

Design and fabrication of vertically-integrated CMOS image sensors.

Technologies to fabricate integrated circuits (IC) with 3D structures are an emerging trend in IC design. They are based on vertical stacking of active components to form heterogeneous microsystems. Electronic image sensors will benefit from these technologies because they allow increased pixel-level data processing and device optimization.