Depth of field (DOF), the range of scene depths that appear sharp in a photograph, poses a fundamental tradeoff in photography---wide apertures are important to reduce imaging noise, but they also increase defocus blur. Recent advances in computational imaging modify the acquisition process to extend the DOF through deconvolution. Because deconvolution quality is a tight function of the frequency power spectrum of the defocus kernel, designs with high spectra are desirable. In this paper we study how to design effective extended-DOF systems, and show an upper bound on the maximal power spectrum that can be achieved. We analyze defocus kernels in the 4D light field space and show that in the frequency domain, only a low-dimensional 3D manifold contributes to focus. Thus, to maximize the defocus spectrum, imaging systems should concentrate their limited energy on this manifold. We review several computational imaging systems and show either that they spend energy outside the focal manifold or do not achieve a high spectrum over the DOF. Guided by this analysis we introduce the lattice-focal lens, which concentrates energy at the low-dimensional focal manifold and achieves a higher power spectrum than previous designs. We have built a prototype lattice-focal lens and present extended depth of field results.

Abstract We present a technique for approximating isotropic BRDFs and precomputed self‐occlusion that enables accurate and efficient prefiltered environment map rendering. Our approach uses a nonlinear approximation of the BRDF as a weighted sum of isotropic Gaussian functions. Our representation requires a minimal amount of storage, can accurately represent BRDFs of arbitrary sharpness, and is above all, efficient to render. We precompute visibility due to self‐occlusion and store a low‐frequency approximation suitable for glossy reflections. We demonstrate our method by fitting our representation to measured BRDF data, yielding high visual quality at real‐time frame rates.

The emergent field of computational photography is proving that, by coupling generalized imaging optics with software processing, the quality and flexibility of imaging systems can be increased. In this paper, we capture and manipulate multiple images of a scene taken with different aperture settings ( f -numbers). We design and implement a prototype optical system and associated algorithms to capture four images of the scene in a single exposure, each taken with a different aperture setting. Our system can be used with commercially available DSLR cameras and photographic lenses without modification to either. We leverage the fact that defocus blur is a function of scene depth and f /# to estimate a depth map. We demonstrate several applications of our multi-aperture camera, such as post-exposure editing of the depth of field, including extrapolation beyond the physical limits of the lens, synthetic refocusing, and depth-guided deconvolution.

We present an automated approach for high-quality preview of feature-film rendering during lighting design. Similar to previous work, we use a deep-framebuffer shaded on the GPU to achieve interactive performance. Our first contribution is to generate the deep-framebuffer and corresponding shaders automatically through data-flow analysis and compilation of the original scene. Cache compression reduces automatically-generated deep-framebuffers to reasonable size for complex production scenes and shaders. We also propose a new structure, the indirect framebuffer , that decouples shading samples from final pixels and allows a deep-framebuffer to handle antialiasing, motion blur and transparency efficiently. Progressive refinement enables fast feedback at coarser resolution. We demonstrate our approach in real-world production.

We propose a real-time method for rendering rigid objects with complex view-dependent effects under distant all-frequency lighting. Existing precomputed light transport approaches can render rich global illumination effects, but high-frequency view-dependent effects such as sharp highlights remain a challenge. We introduce a new representation of the light transport operator based on sums of Gaussians. The nonlinear parameters of our representation enable 1) arbitrary bandwidth because scale is encoded as a direct parameter, and 2) high-quality interpolation across view and mesh triangles because we interpolate the mean direction of the Gaussians, thereby preventing linear cross-fading artifacts. However, fitting the precomputed light transport data to this new representation requires solving a nonlinear regression problem that is more involved than traditional linear and nonlinear (truncation) approximation techniques. We present a new data fitting method based on optimization that includes energy terms aimed at enforcing artifact-free interpolation. We demonstrate that our method achieves high visual quality with a small storage cost and an efficient rendering algorithm.