Imaging in the underwater environment suffers from color degradation and poor visibility since the light spectrum is selectively absorbed and scattered by water and floating particles. In this paper we introduce a simple but effective underwater dehazing approach that builds on an original color transfer strategy to align the color statistics of a hazy input to the ones of a reference image, also captured underwater, but with neglectable water attenuation. As an original specificity, our proposed color transfer approach is designed to promote the preservation of salient regions, as well as of the details obtained by subtracting from the input an edge-preserving smoothed version of itself. The color-transferred input is then restored by inverting a simplified version of the McGlamery underwater image formation model, using the conventional Dark Channel Prior to estimate the transmission map and the back-scattered light parameter involved in the model. We demonstrate that our color transfer step is crucial for a good transmission estimation but mostly for underwater dehazing where other specialized techniques fail. Extensive qualitative and quantitative results demonstrate the effectiveness of the proposed approach to estimate the transmission, including for cases where traditional specialized techniques fail, and to improve the image quality.

Abstract This paper presents an efficient acceleration technique for the global line radiosity Multipath method. In this approach, the scene is subdivided in a hierarchy of box bounded subscenes, the boxes subdivided in a grid of virtual patches which store angular information. A new recursive (according to the hierarchy of subscenes) function allows to execute the Multipath algorithm at different levels of the hierarchy. A speed up factor up to 3–4 has been obtained on some of the tested scenes.

In this paper a new gradient estimation method is presented which is based on linear regression. Previous contextual shading techniques try to fit an approximate function to a set of surface points in the neighborhood of a given voxel. Therefore a system of linear equations has to be solved using the computationally expensive Gaussian elimination. In contrast, our method approximates the density function itself in a local neighborhood with a 3D regression hyperplane. This approach also leads to a system of linear equations but we will show that it can be solved with an efficient convolution. Our method provides at each voxel location the normal vector and the translation of the regression hyperplane which are considered as a gradient and a filtered density value respectively. Therefore this technique can be used for surface smoothing and gradient estimation at the same time.

The paper presents simple, physically plausible, but not physically based reflectance models for metals and other specular materials. So far there has been no metallic BRDF model that is easy to compute, suitable for fast importance sampling and is physically plausible. This gap is filled by appropriate modifications of the Phong, Blinn and the Ward models. The Phong and the Blinn models are known not to have metallic characteristics. On the other hand, this paper also shows that the Cook‐Torrance and the Ward models are not physically plausible, because of their behavior at grazing angles. We also compare the previous and the newly proposed models. Finally, the generated images demonstrate how the metallic impression can be provided by the new models.

We introduce a physically plausible mathematical model for a large class of BRDFs. The new model is as simple as the well‐known Phong model, but eliminates its disadvantages. It gives a good visual approximation for many practical materials: coated metals, plastics, ceramics, retro‐reflective paints, anisotropic and retro‐reflective materials, etc. Because of its illustrative properties it can be used easily in most commercial software and because of its low computational cost it is practical for virtual reality. The model is based on a special basic BRDF definition, which meets the requirements of reciprocity and of energy conservation. Then a class of BRDFs is constructed from this basic BRDF with different weight functions. The definition of such weight functions requires the user to specify the profile of the highlights, from which the weight function is obtained by derivation. It is also demonstrated how importance sampling can be used with the new BRDFs.

The available contrast of common display devices is much lower than image data often demand. Usually a variant of an average of the field of view is used to normalize the image. An alternative approach is introduced to define the mapping of rendered values to the displayable intensity range. The luminance range is chosen such that a minimum amount of information is lost thereby preserving the contrast ratio of all correctly displayed parts. The loss of information can be regulated by different error functions. If the loss is too large, the luminance range can be increased, but the original contrast is not presented any more. In this case the method represents an improvement of Schlick's (1994) mapping technique. The newly introduced method can be applied on color and gray scale images, rendered in absolute or fictitious units.

Every rendering process consists of two steps. The first is the computing of luminance values by methods like ray tracing or radiosity, and the second step is the mapping of the computed values to values appropriate for displaying. In the last years, as alternative to simple linear scaling which maps the average value to the medium luminance, some new ways of mapping were introduced. These new methods are based on photography analogies and on human vision models. All existing methods follow, implicitly or explicitly, the reflected light metering principle. The method introduced in this paper is the first that follows the incident light metering used in professional photography and in the movie industry. Actually the irradiances are measured using a set of diffusors, which are placed automatically in the scene, and a linear scale factor based on these measurements is used to map the computed radiances to the display device. The diffusors act as half space integrators, they collect the light energy from all half space directions. The light comes from the primary light sources, or it is the result of various interreflections. The newly introduced method reproduces original colors faithfully even for scenes with very low or very high average reflectivity.

A good image metric is often needed in digital image synthesis. It can be used to check the convergence behavior in progressive methods, to compare images rendered using various rendering methods etc. Since images are rendered to be observed by humans, an image metric should correspond to human perception as well. We propose here a new algorithm which operates in the original image space. There is no need for Fourier or wavelet transforms. Furthermore, the new metric is view distance dependent. The new method uses the contrast sensitivity function. The main idea is to place a number of various rectangles in images, and to compute the CIE LUV average color difference between corresponding rectangles. Errors are then weighted according to the rectangle size and the contrast sensitivity function.

In this paper we present a new radiosity algorithm, based on the notion of a well distributed ray set (WDRS). A WDRS is a set of rays, connecting mutually visible points and patches, that forms an approximate representation of the radiosity operator and the radiosity distribution. We propose an algorithm that constructs an optimal WDRS for a given accuracy and mesh. The construction is based on discrete importance sampling as in previously proposed stochastic radiosity algorithms, and on quasi Monte Carlo sampling. Quasi Monte Carlo sampling leads to faster convergence rates and the fact that the sampling is deterministic makes it possible to represent the well distributed ray set very efficiently in computer memory. Like previously proposed stochastic radiosity algorithms, the new algorithm is well suited for computing the radiance distribution in very complex diffuse scenes, when it is not feasible to explicitly compute and store form factors as in classical radiosity algorithms. Experiments show that the new algorithm is often more efficient than previously proposed Monte Carlo radiosity algorithms by half an order of magnitude and more.

This paper presents an enhanced transillumination radiosity method that can provide accurate solutions at relatively low computational cost. The proposed algorithm breaks down the double integral of the gathered power to an area integral that is computed analytically and to a directional integral that is evaluated by quasi‐Monte Carlo techniques. Since the analytical integration results in a continuous function of finite variation, the quasi‐Monte Carlo integration that follows the analytical integration will be efficient and its error can be bounded by the Koksma‐Hlawka inequality. The paper also analyses the requirements of the convergence, presents theoretical error bounds and proposes error reduction techniques. The theoretical bounds are compared with simulation results.

We examine various solutions to the global illumination problem, based on an exact mathematical analysis of the rendering equation. In addition to introducing efficient radiosity algorithms, we present a uniform approach to reformulate all of the basic radiosity equations used so far. Using hybrid methods we are able to analyze possible combinations of the view-dependent ray-tracing method and of the low-resolution radiosity-based method, and to offer new algorithms.

Abstract Methods for realistic image synthesis are described. Exact mathematical establishment of reflectance models is presented, together with the development of feasible reflectance models meeting the energy balance. A new bidirectional reflectance type, separable reflectance, is introduced. Necessary and sufficient conditions for the iterative solution of the linear equation system of interreflection are presented. Lastly, a new method of inserting numerically defined objects into real optical surroundings is described.