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When Does Computational Imaging Improve Performance?
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DOI：10.1109/TIP.2012.2216538.png)
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Abstract
En 中文
A number of computational imaging techniques are introduced to improve image quality by increasing light throughput. These techniques use optical coding to measure a stronger signal level. However, the performance of these techniques is limited by the decoding step, which amplifies noise. Although it is well understood that optical coding can increase performance at low light levels, little is known about the quantitative performance advantage of computational imaging in general settings. In this paper, we derive the performance bounds for various computational imaging techniques. We then discuss the implications of these bounds for several real-world scenarios (e.g., illumination conditions, scene properties, and sensor noise characteristics). Our results show that computational imaging techniques do not provide a significant performance advantage when imaging with illumination that is brighter than typical daylight. These results can be readily used by practitioners to design the most suitable imaging systems given the application at hand.
Keywords:
Computational imaging
computational photography
deconvolution
defocus deblurring
denoising
extended depth of field
image priors
image restoration
motion deblurring
multiplexing
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W