Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs

Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception. In computer vision, the two problems are traditionally solved in separate tracks. In this paper, we present a unified learning-based technique that simultaneously uses both types of cues for depth inference. Specifically, we use a pair of focal stacks as input to emulate human perception. We first construct a comprehensive focal stack training dataset synthesized by depth-guided light field rendering. We then construct three individual networks: a Focus-Net to extract depth from a single focal stack, a EDoF-Net to obtain the extended depth of field (EDoF) image from the focal stack, and a Stereo-Net to conduct stereo matching. We show how to integrate them into a unified BDfF-Net to obtain high-quality depth maps. Comprehensive experiments show that our approach outperforms the state-of-the-art in both accuracy and speed and effectively emulates human vision systems

Effect of probe energy and competing pathways on time-resolved photoelectron spectroscopy signals: ring-opening reaction of 1,3-cyclohexadiene

The ring-opening dynamics of 1,3-cyclohexadiene (CHD) following UV excitation is studied using a model based on quantum molecular dynamics simulations with the ab-initio multiconfigurational Ehrenfest (AI-MCE) method coupled to the Dyson orbital approach for photoionisation cross sections. Time-dependent photoelectron spectra are calculated for probe photon energies in the range 2-15 eV. The calculations demonstrate the value of universal high-energy probes, capableof tracking the full photochemical dynamics of the molecule, as well as the benefit of more selective, lower-energy probes. The predicted signal, especially with the universal probes, becomes highly convoluted due to the contributions from multiple reaction paths, rendering interpretationdifficult unless complementary measurements and theoretical comparisons are available

Dating Historical Color Images

Abstract. We introduce the task of automatically estimating the age of historical color photographs. We suggest features which attempt to capture temporally discriminative information based on the evolution of color imaging processes over time and evaluate the performance of both these novel features and existing features commonly utilized in other problem domains on a novel historical image data set. For the challenging classification task of sorting historical color images into the decade during which they were photographed, we demonstrate significantly greater accuracy than that shown by untrained humans on the same data set. Additionally, we apply the concept of data-driven camera response function estimation to historical color imagery, demonstrating its relevance to both the age estimation task and the popular application of imitating the appearance of vintage color photography.

Radial Bright Channel Prior for Single Image Vignetting Correction

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