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In this work, we investigate the joint optimization of achromatic DOE and image processing using a full differentiable optimization model that maps the actual source image to the reconstructed one. This model includes wavelength-dependent propagation block, sensor sampling block, and imaging processing block. We jointly optimize the physical height of DOEs and the parameters of image processing block to minimize the errors over a hyperspectral image dataset. We simplify the rotational symmetric DOE to 1D profle to reduce the computational complexity of 2D propagation. The joint optimization is implemented using auto differentiation of Tensor ow to compute parameter gradients. Simulation results show that the proposed joint design outperforms conventional methods in preserving higher image fidelity. |
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Diffractive optical elements
Point spread functions
Sensors
Image processing
Lens design
Image sensors
Computational imaging
