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This study explores the advancements in Computational Imaging (C.I.) systems, which transcend conventional imaging methodologies by integrating innovative hardware and reconstruction algorithms. Focusing on the challenge of balancing the field of view (FOV) and resolution in microscopy due to sensor size limitations, we have introduced the Multiple Point Impulse Response (MPIR) as a Point Spread Function (PSF) engineering technique aimed at extending the FOV in our previous work. Despite encountering scrambled images, the utilization of sparse recovery algorithms successfully reconstructs object information beyond the sensor's active area. Additionally, increasing impulse density improves reconstruction quality, although practical sensor constraints, notably limited bit depth, impact contrast, and overall reconstruction quality. Here, we have shown that with a trade-off in image fidelity and data information content, it is possible to overcome the limitation imposed by the sensor bit-depth.
Malik et al. (Tue,) studied this question.