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The ability to image high-dynamic-range (HDR) scenes is crucial in manycomputer vision applications. The dynamic range of conventional sensors,however, is fundamentally limited by their well capacity, resulting insaturation of bright scene parts. To overcome this limitation, emerging sensorsoffer in-pixel processing capabilities to encode the incident irradiance. Amongthe most promising encoding schemes is modulo wrapping, which results in acomputational photography problem where the HDR scene is computed by anirradiance unwrapping algorithm from the wrapped low-dynamic-range (LDR) sensorimage. Here, we design a neural network--based algorithm that outperformsprevious irradiance unwrapping methods and we design a perceptually inspired"mantissa" encoding scheme that more efficiently wraps an HDR scene into an LDRsensor. Combined with our reconstruction framework, MantissaCam achievesstate-of-the-art results among modulo-type snapshot HDR imaging approaches. Wedemonstrate the efficacy of our method in simulation and show benefits of ouralgorithm on modulo images captured with a prototype implemented with aprogrammable sensor.

MantissaCam: Learning Snapshot High-dynamic-range Imaging with Perceptually-based In-pixel Irradiance Encoding