Compressive Higher-order Sparse and Low-Rank Acquisition with a Hyperspectral Light Stage

Compressive sparse and low-rank recovery (CSLR) is a novel method for compressed sensing deriving a low-rank and a sparse data terms from randomized projection measurements. While previous approaches either applied compressive measurements to phenomena assumed to be sparse or explicitly assume and measure low-rank approximations, CSLR is inherently robust if any such assumption might be violated. In this paper, we will derive CSLR using Fixed-Point Continuation algorithms, and extend this approach in order to exploit the correlation in high-order dimensions to further reduce the number of captured samples. Though generally applicable, we demonstrate the effectiveness of our approach on data sets captured with a novel hyperspectral light stage that can emit a distinct spectrum from each of the 196 light source directions enabling bispectral measurements of reflectance from arbitrary viewpoints. Bispectral reflectance fields and BTFs are faithfully reconstructed from a small number of compressed measurements

Demosaicing by smoothing along 1D features

Most digital cameras capture color pictures in the form of an image mosaic, recording only one color channel at each pixel position. Therefore, an interpolation algorithm needs to be applied to reconstruct the missing color information. In this paper we present a novel Bayer pattern demosaicing approach, employing stochastic global optimization performed on a pixel neighborhood. We are minimizing a newly developed cost function that increases smoothness along one-dimensional image features. While previous algorithms have been developed focusing on LDR images only, our optimization scheme and the underlying cost function are designed to handle both LDR and HDR images, creating less demosaicing artifacts, compared to previous approaches. 1

Printing Spatially-Varying Reflectance

Although real-world surfaces can exhibit significant variation in materials --- glossy, diffuse, metallic, etc. --- printers are usually used to reproduce color or gray-scale images. We propose a complete system that uses appropriate inks and foils to print documents with a variety of material properties. Given a set of inks with known Bidirectional Reflectance Distribution Functions (BRDFs), our system automatically finds the optimal linear combinations to approximate the BRDFs of the target documents. Novel gamut-mapping algorithms preserve the relative glossiness between different BRDFs, and halftoning is used to produce patterns to be sent to the printer. We demonstrate the effectiveness of this approach with printed samples of a number of measured spatially-varying BRDFs