High-Quality Computational Imaging Through Simple Lenses
High-Quality Computational Imaging Through Simple Lenses Felix Heide1, Mushfiqur Rouf1, Matthias B. Hullin1, Bjorn¨ Labitzke2, Wolfgang Heidrich1, Andreas Kolb2 1University of British Columbia, 2University of Siegen Fig. 1. Our system reliably estimates point spread functions of a given optical system, enabling the capture of high-quality imagery through poorly performing lenses. From left to right: Camera with our lens system containing only a single glass element (the plano-convex lens lying next to the camera in the left image), unprocessed input image, deblurred result. Modern imaging optics are highly complex systems consisting of up to two pound lens made from two glass types of different dispersion, i.e., dozen individual optical elements. This complexity is required in order to their refractive indices depend on the wavelength of light differ- compensate for the geometric and chromatic aberrations of a single lens, ently. The result is a lens that is (in the first order) compensated including geometric distortion, field curvature, wavelength-dependent blur, for chromatic aberration, but still suffers from the other artifacts and color fringing. mentioned above. In this paper, we propose a set of computational photography tech- Despite their better geometric imaging properties, modern lens niques that remove these artifacts, and thus allow for post-capture cor- designs are not without disadvantages, including a significant im- rection of images captured through uncompensated, simple optics which pact on the cost and weight of camera objectives, as well as in- are lighter and significantly less expensive. Specifically, we estimate per- creased lens flare. channel, spatially-varying point spread functions, and perform non-blind In this paper, we propose an alternative approach to high-quality deconvolution with a novel cross-channel term that is designed to specifi- photography: instead of ever more complex optics, we propose cally eliminate color fringing.
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