Peyman Fayyaz Shahandashti

In the rapidly evolving field of computational imaging, the coherent codesign of image sensors and signal/image processing unlocks numerous new capabilities. To retrieve sparse macroscopic impulse response functions (IRF) to modulated light we integrate advanced circuit design with signal processing techniques. We introduce a novel sensor design that enables the realization of a single-shot Fourier camera, alongside computationally efficient methods for sparse IRF recovery. The camera uses optimal (Fourier) sensing kernels to capture sparse signals, all while maintaining an affordable cost in terms of spatial resolution. This approach has applications in various fields, including multi-path Time-of-Flight (ToF) imaging, transient imaging, ultrafast photography of sparse events, high-energy particle detection, and non-line-of-sight (NLOS) imaging. In this webinar, we focus particularly on resolving multipath interference (MPI), where light reflects off multiple surfaces before reaching the sensor. The aim is to capture the IRF of a macroscopic scene, which is often sparse or highly compressible. Resolving MPI requires bandwidth, and since conventional ToF cameras often rely on a single frequency to reconstruct a single return path, they fail in the presence of MPI. By leveraging multifrequency data, the Fourier camera can effectively distinguish direct and indirect light paths, thereby improving the accuracy and reliability of depth information at high speed, ensuring efficient and accurate 3D imaging in real-time applications.