HDR Imaging Using Augmented Lagrange Multipliers (ALM)

Figure 7: The top row shows color transfer results using our test images when all three of the ℓ αβ channels are trans- ferred from source to destination.. The bottom row shows

Figure 2: Comparison under a positional spot light of a geom- etry model (top left), depth augmented billboard (top middle) and a standard normal mapped billboard (top right)..

The top row of images was rendered using the standard diffusion dipole (DD), the second row using Monte Carlo path tracing (MC), while the third row uses sources distributed along

We use the approach described in the object removal case to approx- imate the color and depth information in the new viewpoints, and then apply the video reconstruction algorithm

(a) calibration pattern (b) image with open aperture (small f-number) (c) image with “star-shaped” aperture Figure 4: Example of optical vignetting calibration using a

Proposed for the registration of multi modal medical images, in the last years MI has been adapted to align a 3D model to a given image by using different renderings of the model and

Figure 6: PFC input image (left), segmentation in eight grains obtained via the classical Chan–Vese approach (middle, result from [Ber10, Figure 3.14]) and our result by using F 2 ,

Figure 1: An example of a scene rendered using SSAO: (left) the diffuse lighting of the scene, (middle) the ambient occlu- sion factor for the scene calculated using SSAO and