The problem of estimating spectral reflectances from the responses of a digital camera has received considerable attention recently. This problem can be cast as a regularized regression problem or as a statistical inversion problem. We discuss some previously suggested estimation methods based on critically undersampled RGB measurements and describe some relations between them. We concentrate mainly on those models that are using a priori information in the form of high-resolution measurements. We use the "kernel machine" framework in our evaluations and concentrate on the use of multiple illuminations and on the investigation of the performance of global and locally adapted estimation methods. We also introduce a nonlinear transformation of reflectance values to ensure that the estimated reflection spectra fulfill physically motivated boundary conditions. The reported experimental results are derived from measured and simulated camera responses from the Munsell Matte, NCS, and Pantone data sets.
A comprehensive experimental study was carried out to replicate sub‐micron features using the injection molding technique. For the experiments, five different plastic materials were selected according to their flow properties. The materials were polycarbonate (PC), styrene‐butadiene block copolymer (SBS), impact modified poly(methyl methacrylate), methyl methacrylate‐acrylonitrile‐butadiene‐styrene polymer (MABS), and cyclic olefin copolymer (COC). Nanofeatures down to 200‐nm line width and with aspect ratios (aspect ratio = depth/width) of 1:1 could be replicated. In all selected materials, the greatest differences between the materials emerged when the aspect ratio increased to 2:1. The most favorable results were obtained with the use of high flow polycarbonate as the molding material. The best replication results were achieved when melt and mold temperatures were higher than normal values.
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