Filters with slowly decaying impulse responses have many uses in computer graphics. Recursive filters are often the fastest option for such cases. In this paper, we derive closed-form formulas for computing the exact initial feedbacks needed for recursive filtering infinite input extensions. We provide formulas for the constant-padding (e.g. clamp-to-edge), periodic (repeat) and even-periodic (mirror or reflect) extensions. These formulas were designed for easy integration into modern block-parallel recursive filtering algorithms. Our new modified algorithms are state-of-the-art, filtering images faster even than previous methods that ignore boundary conditions.

No abstract available.

Image processing operations like blurring, inverse convolution, and summed-area tables are often computed efficiently as a sequence of 1D recursive filters. While much research has explored parallel recursive filtering, prior techniques do not optimize across the entire filter sequence. Typically, a separate filter (or often a causal-anticausal filter pair) is required in each dimension. Computing these filter passes independently results in significant traffic to global memory, creating a bottleneck in GPU systems. We present a new algorithmic framework for parallel evaluation. It partitions the image into 2D blocks, with a small band of additional data buffered along each block perimeter. We show that these perimeter bands are sufficient to accumulate the effects of the successive filters. A remarkable result is that the image data is read only twice and written just once, independent of image size, and thus total memory bandwidth is reduced even compared to the traditional serial algorithm. We demonstrate significant speedups in GPU computation.

Abstract We present a flexible and highly efficient hardware‐assisted volume renderer grounded on the original Projected Tetrahedra (PT) algorithm. Unlike recent similar approaches, our method is exclusively based on the rasterization of simple geometric primitives and takes full advantage of graphics hardware. Both vertex and geometry shaders are used to compute the tetrahedral projection, while the volume ray integral is evaluated in a fragment shader; hence, volume rendering is performed entirely on the GPU within a single pass through the pipeline. We apply a CUDA‐based visibility ordering achieving rendering and sorting performance of over 6 M Tet/s for unstructured datasets. Furthermore, as each tetrahedron is processed independently, we employ a data‐parallel solution which is neither bound by GPU memory size nor does it rely on auxiliary volume information. In addition, iso‐surfaces can be readily extracted during the rendering process, and time‐varying data are handled without extra burden.

Direct volume rendering has become a popular technique for visualizing volumetric data from sources such as scientific simulations, analytic functions, medical scanners, among others. Volume rendering algorithms, such as raycasting, can produce high-quality images, however, the use of raycasting has been limited due to its high demands on computational power and memory bandwidth. In this paper, we propose a new implementation of the raycasting algorithm that takes advantage of the highly parallel architecture of the Cell Broadband Engine processor, with 9 heterogeneous cores, in order to allow efficient raycasting of irregular datasets. All the computational power of the Cell BE processor, though, comes at the cost of a different programming model. Applications need to be rewritten, which requires using multithreading and vectorized code. In our approach, we tackle this problem by distributing ray computations using the visible faces, and vectorizing the lighting integral operations inside each core. Our experimental results show that we can obtain good speedups reducing the overall rendering time significantly.

Abstract We present an efficient Graphics Processing Unit GPU‐based implementation of the Projected Tetrahedra (PT) algorithm. By reducing most of the CPU–GPU data transfer, the algorithm achieves interactive frame rates (up to 2.0 M Tets/s) on current graphics hardware. Since no topology information is stored, it requires substantially less memory than recent interactive ray casting approaches. The method uses a two‐pass GPU approach with two fragment shaders. This work includes extended volume inspection capabilities by supporting interactive transfer function editing and isosurface highlighting using a Phong illumination model.