Technical Papers
Image Processing
Tuesday, 11 August 3:45 PM - 5:35 PM | Los Angeles Convention Center, Room 150/151 Session Chair: Steve Lin, Microsoft Research Asia
Tuesday, 11 August 3:45 PM - 5:35 PM | Los Angeles Convention Center, Room 150/151 Session Chair: Steve Lin, Microsoft Research Asia
This paper proposes a perceptually based method for downscaling images that provides a better apparent depiction of the input image. The downscaled images retain perceptually important features and details, and are spatio-temporally consistent. The algorithm has a simple, efficient, and parallelizable implementation, with running times similar to linear filtering.
Cengiz Oztireli
ETH Zürich
Markus Gross
ETH Zürich
This paper proposes modified color lines prior for image dehazing. The new prior allows assessment of its validity across the image and avoids mispredictions where it fails to take place. Extensive evaluation of the new method demonstrates its greater accuracy.
Raanan Fattal
Hebrew University of Jerusalem
This paper introduces piecewise image flattening, which preserves salient edges and contours while eliminating insignificant details, producing a nearly piecewise constant image with sparse structures. Image flattening facilitates both edge-preserving smoothing and intrinsic image decomposition. The decomposed images have applications in surface re-texturing and object insertion into photographs.
Sai Bi
The University of Hong Kong
Xiaoguang Han
The University of Hong Kong
Yizhou Yu
The University of Hong Kong
A data-driven method for removing soft shadows from RGB photographs. After the user selects a region using broad brush strokes, the algorithm automatically reconstructs the underlying matte and enables removal or modification of the shadow.
Maciej Gryka
University College London
Michael Terry
University of Waterloo
Gabriel J. Brostow
University College London
A unified computational approach for taking photos through reflecting or occluding visual obstructions, such as windows and fences. From a short image sequence, the algorithm leverages motion parallax to automatically remove these visual obstructions.
Tianfan Xue
Microsoft Corporation, Massachusetts Institute of Technology
Michael Rubinstein
Google Inc.
Liu Ce
Google Inc.
William Freeman
Massachusetts Institute of Technology