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Columbia University published Oliver Cossairt's PhD thesis "
Tradeoffs and Limits in Computational Imaging" reviewing broad range of modern technologies, such as EDoF, plenoptic cameras, gigapixel cameras, etc. Some conclusions from the thesis:
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| Figure 1.7: EDOF cameras sacrifice best case performance for average case performance. The performance is measured as the MTF of the camera system as a function of depth. |
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| Figure 2.1: Simulated image performance for three EDOF cameras. An IEEE resolution chart is placed at different depths. The aperture size A and defocus slope in light field space s0 are chosen so that the maximum defocus blur diameter is 100 pixels. The center PSF is used for deblurring, producing the images shown in (b). Close-ups in (c) show that the sharpest image is produced by wavefront coding at the center depth (s0A = 0). However, wavefront coding produces significant deblurring artifacts for defocus values as small as s0A = 33 pixels, while diffusion coding produces near identical results for the entire depth range. |
Spectral Focal Sweep EDoF technology is proposed, similar to DxO one, but said to perform better:
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| Figure 1.9: Performance vs. Complexity for the Spectral Focal Sweep camera (see Chapter 3). A conventional camera achieves higher performance than the Spectral Focal Sweep camera, but at the cost of a significant increase in complexity. |
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| Figure 1.8: Resolution scales rapidly with camera size for ideal diffraction limited lenses. However, in practice, resolution reaches a plateau due to geometric aberrations. The Gigapixel Computational Camera introduced in Chapter 3 breaks the aberration limit so that resolution continues to increase with camera size, despite the presence of geometric aberrations. |
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| Figure 4.6: For conventional lens designs, the F/# typically scales with the cube root of the focal length in millimeters. |
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| Figure 4.11: Scaling laws for computational imaging systems with spherical aberrations. The Rana, which was analytically derived, shows an improvement upon the aberration limited curve Rgeom, without requiring F/# to increase with M. Performance is further improved when natural image priors are taken into account, as the Rprior curve shows. The Rprior curve improves upon the conventional lens design curve Rconv, also without requiring F/# to increase with M. |
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