Transmittance Regularizer for Binary coded Aperture Design in a Computational Imaging end-to-end Approach

Deep learning End-to-End (E2E) approaches have emerged as alternative optical design models, which jointly train the optical parameters of the sensing protocol, and the parameters of the deep neural network to achieve a specific task. This E2E model is particularly useful in the design of coding optical systems to address relevant constraints of the coded aperture (CA) design. To name, recent works address the binary constraint by incorporating regularization functions in the E2E optimization problem to promote binary value entries. How-ever, they do not consider other important CA assembling properties as the transmittance level, which plays a crucial role in implementable setups. Therefore, this work proposes two transmittance regularizers that jointly induce binary en-tries and adjust the transmittance level to be incorporated in an E2E approach. In particular, one of the regularizers allows achieving an exact value of the transmittance level when required for specific applications.