IEEE Trans Image Process - When does computational imaging improve performance?

Tópicos

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Resumo

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.

Resumo Limpo

number comput imag techniqu introduc improv imag qualiti increas light throughput techniqu use optic code measur stronger signal level howev perform techniqu limit decod step amplifi nois although well understood optic code can increas perform low light level littl known quantit perform advantag comput imag general set paper deriv perform bound various comput imag techniqu discuss implic bound sever realworld scenario eg illumin condit scene properti sensor nois characterist result show comput imag techniqu provid signific perform advantag imag illumin brighter typic daylight result can readili use practition design suitabl imag system given applic hand

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