Since the birth of charge coupled devices (CCD) and the complementary metal-oxide-semiconductor (CMOS) active pixel sensors, pixel pitch of digital image sensors has been continuously shrinking to meet the resolution and size requirements of the cameras. However, shrinking pixels reduces the maximum number of photons a sensor can hold, a phenomenon broadly known as the full-well capacity limit. The drop in full-well capacity causes drop in signal-to-noise ratio and dynamic range.<div><br></div><div>The Quanta Image Sensor (QIS) is a class of solid-state image sensors proposed by Eric Fossum in 2005 as a potential solution for the limited full-well capacity problem. QIS is envisioned to be the next generation image sensor after CCD and CMOS since it enables sub-diffraction-limit pixels without the inherited problems of pixel shrinking. Equipped with a massive number of detectors that have single-photon sensitivity, the sensor counts the incoming photons and triggers a binary response “1” if the photon count exceeds a threshold, or “0” otherwise. To acquire an image, the sensor oversamples the space and time to generate a sequence of binary bit maps. Because of this binary sensing mechanism, the full-well capacity, signal-to-noise ratio and the dynamic range can all be improved using an appropriate image reconstruction algorithm. The contribution of this thesis is to address three image processing problems in QIS: 1) Image reconstruction, 2) Threshold design and 3) Color filter array design.</div><div><br></div><div>Part 1 of the thesis focuses on reconstructing the latent grayscale
image from the QIS binary measurements. Image reconstruction is a
necessary step for QIS because the raw binary measurements are not
images. Previous methods in the literature use iterative algorithms
which are computationally expensive. By modeling the QIS binary
measurements as quantized Poisson random variables, a new non-iterative
image reconstruction method based on the Transform-Denoise framework is
proposed. Experimental results show that the new method produces better
quality images while requiring less computing time.</div><div><br></div><div>Part 2 of the thesis considers the threshold design problem of a QIS. A
spatially-varying threshold can significantly improve the reconstruction
quality and the dynamic range. However, no known method of how to
achieve this can be found in the literature. The theoretical analysis of
this part shows that the optimal threshold should match with the
underlying pixel intensity. In addition, the analysis proves the
existence of a set of thresholds around the optimal threshold that give
asymptotically unbiased reconstructions. The asymptotic unbiasedness has
a phase transition behavior. A new threshold update scheme based on
this idea is proposed. Experimentally, the new method can provide good
estimates of the thresholds with less computing budget compared to
existing methods.</div><div><br></div><div>Part 3 of the thesis extends QIS capabilities to color imaging by
studying how a color filter array should be designed. Because of the
small pixel pitch of QIS, crosstalk between neighboring pixels is
inevitable and should be considered when designing the color filter
arrays. However, optimizing the light efficiency while suppressing
aliasing and crosstalk in a color filter array are conflicting tasks. A
new optimization framework is proposed to solve the problem. The new
framework unifies several mainstream design criteria while offering
generality and flexibility. Extensive experimental comparisons
demonstrate the effectiveness of the framework.</div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8429048 |
| Date | 13 August 2019 |
| Creators | Omar A Elgendy (6905153) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Image_Processing_for_Quanta_Image_Sensors/8429048 |
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