Compressive Volume Rendering

Compressive rendering refers to the process of reconstructing a full image from a small subset of the rendered pixels, thereby expediting the rendering task. Images produced via direct volume rendering are usually highly compressible in a transform domain such as the Fourier or wavelet domains. In this dissertation, we empirically investigate four image order tech- niques for compressive rendering that are suitable for direct volume rendering. The first technique is based on the theory of compressed sensing and leverages the sparsity of the image gradient in the Fourier domain. Following this, we investigate sparse representation of volume rendered images via dictionary learning. The latter techniques exploit smoothness properties of the rendered image; the third technique recovers the missing pixels via a to- tal variation minimization procedure while the fourth technique incorporates a smoothness prior in a variational reconstruction framework employing interpolating cubic B-splines. We compare and contrast these four techniques in terms of quality, efficiency and sensitivity to the distribution of pixels. Our results show that smoothness-based techniques significantly outperform techniques that are based on compressed sensing as well as dictionary learning and are also robust in the presence of highly incomplete information. We achieve high quality recovery with as little as 20% of the pixels distributed uniformly in screen space

Compressive Volume Rendering

Compressive rendering refers to the process of reconstructing a full image from a small subset of the rendered pixels, thereby expediting the rendering task. In this paper, we empirically investigate three image order techniques for compressive rendering that are suitable for direct volume rendering. The first technique is based on the theory of compressed sensing and leverages the sparsity of the image gradient in the Fourier domain. The latter techniques exploit smoothness properties of the rendered image; the second technique recovers the missing pixels via a total variation minimization procedure while the third technique incorporates a smoothness prior in a variational reconstruction framework employing interpolating cubic B-splines. We compare and contrast the three techniques in terms of quality, efficiency and sensitivity to the distribution of pixels. Our results show that smoothness-based techniques significantly outperform techniques that are based on compressed sensing and are also robust in the presence of highly incomplete information. We achieve high quality recovery with as little as 20% of the pixels distributed uniformly in screen space

Compressive Volume Rendering

Compressive rendering refers to the process of reconstructing a full image from a small subset of the rendered pixels, thereby expediting the rendering task. In this paper, we empirically investigate three image order techniques for compressive rendering that are suitable for direct volume rendering. The first technique is based on the theory of compressed sensing and leverages the sparsity of the image gradient in the Fourier domain. The latter techniques exploit smoothness properties of the rendered image; the second technique recovers the missing pixels via a total variation minimization procedure while the third technique incorporates a smoothness prior in a variational reconstruction framework employing interpolating cubic B-splines. We compare and contrast the three techniques in terms of quality, efficiency and sensitivity to the distribution of pixels. Our results show that smoothness-based techniques significantly outperform techniques that are based on compressed sensing and are also robust in the presence of highly incomplete information. We achieve high quality recovery with as little as 20% of the pixels distributed uniformly in screen space

GPU-based Compressive Volume Rendering

There is an increasing demand in the scientific visualization community for high quality real time interactive volume renderers; but the goal of high quality in volume rendering degrades the performance of the volume renderer. The current advancements in graphics hardware has resulted in the adoptation of the GPU as a solution for the degradation issue in a volume renderer. However there is a caveat, with the use of the GPU as a solution; as the GPUs memory size and long data transfer times between CPU and GPU limit the performance of the GPU based volume renderer. The GPU based volume renderer performance issue can be resolved by rendering a subset of the pixels. By reducing the volume of data the computational costs are reduced. Then using a GPU based conjugate gradient solver we can reconstruct the full image which has the same quality as the original image. This dissertation will show how using GPUs and compressive rendering will optimize the performance of a volume renderer