Schneider Fyhn (panprison9)

Spatial resolution is one of the fundamental bottlenecks in the area of time-resolved imaging. Since each pixel measures a scene dependent time-profile, there is a technological limit on the size of pixel arrays that can be simultaneously used to perform measurements. To overcome this barrier, in this paper, we propose a low-complexity, one-bit sensing scheme. On the data capture front, the time-resolved measurements are mapped to a sequence of +1 and -1. This leads to an extremely simple implementation and at the same time poses a new form of information loss. On the image recovery front, our one-bit time-resolved imaging scheme is complemented with a non-iterative recovery algorithm that can handle the case of single and multiple light paths. Extensive computer simulations and physical experiments benchmarked against conventional Time-of-Flight imaging data corroborate our theoretical framework. Thus, our low-complexity alternative to time-resolved imaging can indeed potentially lead to a new imaging methodology.Camera sensors rely on global or rolling shutter functions to expose an image. This fixed function approach severely limits the sensors' ability to capture high-dynamic-range (HDR) scenes and resolve high-speed dynamics. Spatially varying pixel exposures have been introduced as a powerful computational photography approach to optically encode irradiance on a sensor and computationally recover additional information of a scene, but existing approaches rely on heuristic coding schemes and bulky spatial light modulators to optically implement these exposure functions. Here, we introduce neural sensors as a methodology to optimize per-pixel shutter functions jointly with a differentiable image processing method, such as a neural network, in an end-to-end fashion. Moreover, we demonstrate how to leverage emerging programmable and re-configurable sensor-processors to implement the optimized exposure functions directly on the sensor. Our system takes specific limitations of the sensor into account to optimize physically feasible optical codes and we demonstrate state-of-the-art performance for HDR and high-speed compressive imaging in simulation and experimentallly with real scenes.Lensless cameras, while extremely useful for imaging in constrained scenarios, struggle with resolving scenes with large depth variations. To resolve this, we propose imaging with a set of mask patterns displayed on a programmable mask, and introduce a computational focusing operator that helps to resolve the depth of scene points. As a result, the proposed imager can resolve dense scenes with large depth variations, allowing for more practical applications of lensless cameras. We also present a fast reconstruction algorithm for scene at multiple depths that reduces reconstruction time by two orders of magnitude. Finally, we build a prototype to show the proposed method improves both image quality and depth resolution of lensless cameras.Fuzzy objects composed of hair, fur, or feather are impossible to scan even with the latest active or passive 3D scanners. We present a novel and practical neural rendering (NR) technique called neural opacity point cloud (NOPC) to allow high quality rendering of such fuzzy objects at any viewpoint. NOPC employs a learning-based scheme to extract geometric and appearance features on 3D point clouds including their opacity. It then maps the 3D features onto virtual viewpoints where a new U-Net based NR manages to handle noisy and incomplete geometry while maintaining translation equivariance. Comprehensive experiments on existing and new datasets show our NOPC can produce photorealistic rendering on inputs from multi-view setups such as a turntable system for hair and furry toy captures.Tensor Principal Component Pursuit (TPCP) is a powerful approach in the Tensor Robust Principal Component Analysis (TRPCA), where the goal is to decompose a data tensor to a low-tubal-rank part plus a sparse residual. TPCP is sho