Image-Based Rendering in the Gradient Domain

We propose a novel gradient domain image-based rendering algorithm for handling complex scenes that may include reflective surfaces. We estimate the depths of image gradients rather than pixels, and obtain a final result in real-time through GPU accellerated Poisson integration using an approximate solution as a data term.

Johannes Kopf, Microsoft Research
Fabian Langguth, Technische Universitaet Darmstadt
Daniel Scharstein, Middlebury College
Richard Szeliski, Microsoft Research
Michael Goesele, Technische Universitaet Darmstadt

Data-driven Hallucination of Different Times of Day from a Single Outdoor Photo

We introduce “time hallucination”: synthesizing a plausible image
at a different time of day from an input image.

YiChang Shih, Massachusetts Institute of Technology
Sylvain Paris, Adobe Research
Fredo Durand, Massachusetts Institute of Technology
William T. Freeman, Massachusetts Institute of Technology

Automatic Noise Modeling for Ghost-free HDR Reconstruction

Reconstructing HDR images of dynamic scenes from multiple exposures is challenging: Moving objects introduce ghosting artifacts.
For avoiding these artifacts, we automatically model the image's noise distribution and then estimate the likelihood that corresponding colors be observations of the same object. Cluttered and fast-moving scenes can now be reconstructed.

Miguel Granados, Max Planck Institute for Informatics
Kwang In Kim, Max Planck Institute for Informatics
James Tompkin, Max Planck Institute for Informatics
Christian Theobalt, Max Planck Institute for Informatics

Patch-based High Dynamic Range Video

In this paper, we propose a novel patch-based optimization algorithm for generating High Dynamic Range (HDR) videos using an off-the-shelf camera. The results presented are high-quality and superior to those produced with existing techniques.

Nima Khademi Kalantari, University of California at Santa Barbara
Eli Shechtman, Adobe Systems Inc.
Connelly Barnes, Adobe Systems Inc.
Soheil Darabi, Adobe Systems Inc.
Dan B Goldman, Adobe Systems Inc.
Pradeep Sen, University of California at Santa Barbara