We present a new technique for the display of high-dynamic-range images, which reduces the contrast while preserving detail. It is based on a two-scale decomposition of the image into a base layer, encoding large-scale variations, and a detail layer. Only the base layer has its contrast reduced, thereby preserving detail. The base layer is obtained using an edge-preserving filter called the bilateral filter. This is a non-linear filter, where the weight of each pixel is computed using a Gaussian in the spatial domain multiplied by an influence function in the intensity domain that decreases the weight of pixels with large intensity differences. We express bilateral filtering in the framework of robust statistics and show how it relates to anisotropic diffusion. We then accelerate bilateral filtering by using a piecewise-linear approximation in the intensity domain and appropriate subsampling. This results in a speed-up of two orders of magnitude. The method is fast and requires no parameter setting.

We present a new method for rendering high dynamic range images on conventional displays. Our method is conceptually simple, computationally efficient, robust, and easy to use. We manipulate the gradient field of the luminance image by attenuating the magnitudes of large gradients. A new, low dynamic range image is then obtained by solving a Poisson equation on the modified gradient field. Our results demonstrate that the method is capable of drastic dynamic range compression, while preserving fine details and avoiding common artifacts, such as halos, gradient reversals, or loss of local contrast. The method is also able to significantly enhance ordinary images by bringing out detail in dark regions.

This paper introduces a simple model for subsurface light transport in translucent materials. The model enables efficient simulation of effects that BRDF models cannot capture, such as color bleeding within materials and diffusion of light across shadow boundaries. The technique is efficient even for anisotropic, highly scattering media that are expensive to simulate using existing methods. The model combines an exact solution for single scattering with a dipole point source diffusion approximation for multiple scattering. We also have designed a new, rapid image-based measurement technique for determining the optical properties of translucent materials. We validate the model by comparing predicted and measured values and show how the technique can be used to recover the optical properties of a variety of materials, including milk, marble, and skin. Finally, we describe sampling techniques that allow the model to be used within a conventional ray tracer.

A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and produces good results for a wide variety of images.

In this paper we present a method for recovering the reflectance properties of all surfaces in a real scene from a sparse set of photographs, taking into account both direct and indirect illumination. The result is a lighting-independent model of the scene's geometry and reflectance properties, which can be rendered with arbitrary modifications to structure and lighting via traditional rendering methods. Our technique models reflectance with a lowparameter reflectance model, and allows diffuse albedo to vary arbitrarily over surfaces while assuming that non-diffuse characteristics remain constant across particular regions. The method's input is a geometric model of the scene and a set of calibrated high dynamic range photographs taken with known direct illumination. The algorithm hierarchically partitions the scene into a polygonal mesh, and uses image-based rendering to construct estimates of both the radiance and irradiance of each patch from the photographic data. The algorithm computes the expected location of specular highlights, and then analyzes the highlight areas in the images by running a novel iterative optimization procedure to recover the diffuse and specular reflectance parameters for each region. Lastly, these parameters are used in constructing high-resolution diffuse albedo maps for each surface.

Human vision operates over about nine orders of magnitude, from starlight at 10-4 candelas/meter 2 to daylight at 10 5 cd/m 2. In any given scene, the eye can adapt comfortably over a smaller range of about four orders of magnitude. This still exceeds the dynamic range of conventional display devices and media, which at best cover a range of about 100:1 – only two orders of magnitude. The rest of the information, which would be perceived in the real world as detail in bright and dark regions, is lost above the maximum display value or below the black level. This limitation has serious ramifications for simulated imagery, especially when it is needed to evaluate visual performance or in virtual reality (VR) environments. Previous tone mapping work by Tumblin and Rushmeier 1, Ward 2, and Ferwerda et al 3 did not consider the question of local adaptation. Chiu et al 4 looked into this problem, but their solution resulted in reverse gradients and did not account for human visual response. In this sketch, we present a new method for mapping scenes and images containing high dynamic range information to conventional (and VR) displays. The technique matches object visibility as its primary goal, meaning that objects visible in the real world will be visible on the display, and conversely, objects not visible in the real world will not be visible on the display. As a secondary goal, the method attempts to reproduce a viewer’s subjective response, meaning that the impression of the displayed image should correlate well with memories of the actual scene.

High contrast scenes are difficult to depict on low contrast displays without loss of important fine details and textures. Skilled artists preserve these details by drawing scene contents in coarseto-fine order using a hierarchy of scene boundaries and shadings. We build a similar hierarchy using multiple instances of a new low curvature image simplifier (LCIS), a partial differential equation inspired by anisotropic diffusion. Each LCIS reduces the scene to many smooth regions that are bounded by sharp gradient discontinuities, and a single parameter K chosen for each LCIS controls region size and boundary complexity. With a few chosen K values (K1>K2>K3:::) LCIS makes a set of progressively simpler images, and image differences form a hierarchy of increasingly important details, boundaries and large features. We construct a high detail, low contrast display image from this hierarchy by compressing only the large features, then adding back all small details. Unlike linear filter hierarchies such as wavelets, filter banks, or image pyramids, LCIS hierarchies do not smooth across scene boundaries, avoiding “halo ” artifacts common to previous contrast reducing methods and some tone reproduction operators. We demonstrate LCIS effectiveness on several example images.

This paper proposes a new Bayesian framework for solving the matting problem, i.e. extracting a foreground element from a background image by estimating an opacity for each pixel of the foreground element. Our approach models both the foreground and background color distributions with spatiallyvarying sets of Gaussians, and assumes a fractional blending of the foreground and background colors to produce the final output. It then uses a maximum-likelihood criterion to estimate the optimal opacity, foreground and background simultaneously. In addition to providing a principled approach to the matting problem, our algorithm effectively handles objects with intricate boundaries, such as hair strands and fur, and provides an improvement over existing techniques for these difficult cases.

We present a method, based on pre-computed light transport, for real-time rendering of objects under all-frequency, time-varying illumination represented as a high-resolution environment map. Current techniques are limited to small area lights, with sharp shadows, or large low-frequency lights, with very soft shadows. Our main contribution is to approximate the environment map in a wavelet basis, keeping only the largest terms (this is known as a non-linear approximation). We obtain further compression by encoding the light transport matrix sparsely but accurately in the same basis. Rendering is performed by multiplying a sparse light vector by a sparse transport matrix, which is very fast. For accurate rendering, using non-linear wavelets is an order of magnitude faster than using linear spherical harmonics, the current best technique.

We present a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data. Instead of using analytical reflectance models, we represent each BRDF as a dense set of measurements. This allows us to interpolate and extrapolate in the space of acquired BRDFs to create new BRDFs. We treat each acquired BRDF as a single high-dimensional vector taken from a space of all possible BRDFs. We apply both linear (subspace) and non-linear (manifold) dimensionality reduction tools in an effort to discover a lowerdimensional representation that characterizes our measurements. We let users define perceptually meaningful parametrization directions to navigate in the reduced-dimension BRDF space. On the low-dimensional manifold, movement along these directions produces novel but valid BRDFs.