We present a novel method for aligning images in an HDR (high-dynamic-range) image stack to produce a new exposure stack where all the images are aligned and appear as if they were taken simultaneously, even in the case of highly dynamic scenes. Our method produces plausible results even where the image used as a reference is either too dark or bright to allow for an accurate registration.

Discrete voxel representations are generating growing interest in a wide range of applications in computational sciences and particularly in computer graphics. In this chapter, we first describe an efficient OpenGL implementation of a simple surface voxelization algorithm that produces a regular 3D texture. This technique uses the GPU hardware rasterizer and the new image load/store interface exposed by OpenGL 4.2.

Many computational photography applications require the user to take multiple pictures of the same scene with different camera settings. While this allows to capture more information about the scene than what is possible with a single image, the approach is limited by the requirement that the images be perfectly registered. In a typical scenario the camera is hand-held and is therefore prone to moving during the capture of an image burst, while the scene is likely to contain moving objects.

GPU Gems 3 contains over 40 chapters and nearly 1000 pages full of the latest GPU programming techniques, and includes hundreds of full-color diagrams and pictures. GPU Gems 3 won the Game Developer Magazine's 2007 Front Line Award.

With shrinking process technology, the primary cause of transient faults in semiconductors shifts away from highenergy cosmic particle strikes and toward more mundane and pervasive causes—power fluctuations, crosstalk, and other random noise. Smaller transistor features require a lower critical charge to hold and change bits, which leads to faster microprocessors, but which also leads to higher transient fault rates. Current trends, expected to continue, show soft error rates increasing exponentially at a rate of 8% per technology generation.

GPUs have moved away from the traditional fixed-function 3D graphics pipeline toward a flexible general-purpose computational engine. Today, GPUs can implement many parallel algorithms directly using graphics hardware. Well-suited algorithms that leverage all the underlying computational horsepower often achieve tremendous speedups. Truly, the GPU is the first widely deployed commodity desktop parallel computer

The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability,

have made graphics hardware a compelling platform for computationally demanding tasks in a wide variety

of application domains. In this report, we describe, summarize, and analyze the latest research in mapping

general-purpose computation to graphics hardware.

We begin with the technical motivations that underlie general-purpose computation on graphics processors

A Hardware Redundancy and Recovery Mechanism for Reliable Scientific Computation on Graphics Processors

The graphics processing unit (GPU) has become an integral part of today's mainstream computing systems. Over the past six years, there has been a marked increase in the performance and capabilities of GPUs. The modern GPU is not only a powerful graphics engine but also a highly-parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart.

Existing techniques for fast, high-quality rendering of translucent materials often fix BSSRDF parameters at precomputation time. We present a novel method for accurate rendering and relighting of translucent materials that also enables real-time editing and manipulation of homogeneous diffuse BSSRDFs. We first apply PCA analysis on diffuse multiple scattering to derive a compact basis set, consisting of only twelve 1D functions. We discovered that this small basis set is accurate enough to approximate a general diffuse scattering profile.