Separating Reflection and Transmission Images in the Wild

The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images. Contrary to a common misconception, real-world images are challenging even when polarization information is used. We present a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance, which explicitly uses the polarization properties of light. To train it, we introduce an accurate synthetic data generation pipeline, which simulates realistic reflections, including those generated by curved and non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.Comment: accepted at ECCV 201

An efficient preparation of 1,2-dihydropyridazines through a Diels-Alder/palladium-catalysed elimination sequence

© 2019 Elsevier Ltd A convenient, scalable synthesis of 1,2-dihydropyridazines is presented, based on the Diels-Alder cycloaddition of 1-acetoxy-1,3-butadiene with a variety of azo compounds, followed by a palladium-catalysed elimination. The products are produced on multigram scale and the new method is particularly efficient and atom-economical when compared with previous preparations of 1,2-dihydropyridazines

Molecular mechanics-assisted crystal engineering of solid state photoreactions: application to the Yang photocyclization of a-1-norbornylacetophenone derivatives.

Based on mol. mechanics, Yang photocyclization of a-1-norbornylacetophenone derivs. in the cryst. state was engineered through methylation adjacent to the carbonyl group, thus changing the conformation in the crystal and leading to enhanced diastereo- and enantioselectivity

Enhanced variational image dehazing

Images obtained under adverse weather conditions, such as haze or fog, typically/nexhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling/nthe image structure under the haze layer and recovering vivid colors out of a single image/nremains a challenging task, since the degradation is depth-dependent and conventional methods are/nunable to overcome this problem. In this work, we extend a well-known perception-inspired variational/nframework for single image dehazing. Two main improvements are proposed. First, we replace/nthe value used by the framework for the grey-world hypothesis by an estimation of the mean of/nthe clean image. Second, we add a set of new terms to the energy functional for maximizing the/ninter-channel contrast. Experimental results show that the proposed Enhanced Variational Image/nDehazing (EVID) method outperforms other state-of-the-art methods both qualitatively and quantitatively./nIn particular, when the illuminant is uneven, our EVID method is the only one that recovers/nrealistic colors, avoiding the appearance of strong chromatic artifacts.D. Pardo was partially funded by the Project of the Spanish Ministry of Economy and Competitiveness with reference MTM2013-40824-P, the BCAM “Severo Ochoa” accreditation of excellence SEV-2013-0323, the CYTED 2011 project 712RT0449, and the Basque Government/nConsolidated Research Group Grant IT649-13 on “Mathematical Modeling, Simulation, and Industrial Applications (M2SI)”