A supervised learning approach for dynamic sampling (SLADS) yielded a seven-fold reduction in the number of pixels sampled in hyperspectral Raman microscopy of pharmaceutical materials with negligible loss in image quality (~0.1% error). Following validation with ground-truth samples, sparse sampling strategies were informed in real-time by the preceding set of measurements. In brief, Raman spectra acquired at an initial set of random positions inform the next most information-rich location to subsequently sample within the field of view, which in turn iteratively informs the next locations until a stopping criterion associated with the reconstruction error is met. Calculation times on the order of a few milliseconds were insignificant relative to the timeframe for spectral acquisition at a given sampling location. The SLADS approach has the distinct advantage of being directly compatible with standard Raman instrumentation. Furthermore, SLADS is not limited to Raman imaging, providing a time-savings in image reconstruction whenever the single-pixel measurement time is the limiting factor in image generation.
Shape From Focus (SFF) is the most effective technique for recovering 3D object shape in optical microscopic scenes. Although numerous methods have been recently proposed, less attention has been paid to the quality of source images, which directly affects the accuracy of 3D shape recovery. One of the critical factors impacting the source image quality is the high dynamic range issue, which is caused by the gap between the high dynamic ranges of the real world scenes and the low dynamic range images that the cameras capture. We now present a microscopic 3D shape recovery system based on high dynamic range (HDR) imaging technique. We have conducted experiments on constructing the 3D shapes of difficult-to-image materials such as metal and shiny plastic surfaces, where conventional imaging techniques will have difficulty capturing detail, and will thus result in poor 3D reconstruction. We present experimental results to show that the proposed HDR-based SFF 3D method yields more accurate and robust results than traditional non-HDR techniques for a variety of materials.