Multi-Mask Camera Model for Compressed Acquisition of Light Fields

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Multi-Mask Camera Model for Compressed Acquisition of Light Fields

Hoai-Nam Nguyen; Ehsan Miandji; Christine Guillemot

We present an all-in-one camera model that encompasses the architectures of most existing compressive-sensing light-field cameras, equipped with a single lens and multiple amplitude coded masks that can be placed at different positions between the lens and the sensor. The proposed model, named the equivalent multi-mask camera (EMMC) model, enables the comparison between different camera designs, e.g using monochrome or CFA-based sensors, single or multiple acquisitions, or varying pixel sizes, via a simple adaptation of the sampling operator. In particular, in the case of a camera equipped with a CFA-based sensor and a coded mask, this model allows us to jointly perform color demosaicing and light field spatio-angular reconstruction. In the case of variable pixel size, it allows to perform spatial super-resolution in addition to angular reconstruction. While the EMMC model is generic and can be used with any reconstruction algorithm, we validate the proposed model with a dictionary-based reconstruction algorithm and a regularization-based reconstruction algorithm using a 4D Total-Variation-based regularizer for light field data. Experimental results with different reconstruction algorithms show that the proposed model can flexibly adapt to various sensing schemes. They also show the advantage of using an in-built CFA sensor with respect to monochrome sensors classically used in the literature.

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