This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

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Enhancing Our Lives with Immersive Virtual Reality

Due to the poor lighting condition and limited dynamic range of digital imaging devices, the recorded images are often under-/over-exposed and with low contrast. Most of previous single image contrast enhancement (SICE) methods adjust the tone curve to correct the contrast of an input image. Those methods, however, often fail in revealing image details because of the limited information in a single image. On the other hand, the SICE task can be better accomplished if we can learn extra information from appropriately collected training data. In this paper, we propose to use the convolutional neural network (CNN) to train a SICE enhancer. One key issue is how to construct a training data set of low-contrast and high-contrast image pairs for end-to-end CNN learning. To this end, we build a large-scale multi-exposure image data set, which contains 589 elaborately selected high-resolution multi-exposure sequences with 4,413 images. Thirteen representative multi-exposure image fusion and stack-based high dynamic range imaging algorithms are employed to generate the contrast enhanced images for each sequence, and subjective experiments are conducted to screen the best quality one as the reference image of each scene. With the constructed data set, a CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Experimental results demonstrate the advantages of our method over existing SICE methods with a significant margin.

Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

The Art of Multiprocessor Programming

How best to evaluate a saliency model's ability to predict where humans look in images is an open research question. The choice of evaluation metric depends on how saliency is defined and how the ground truth is represented. Metrics differ in how they rank saliency models, and this results from how false positives and false negatives are treated, whether viewing biases are accounted for, whether spatial deviations are factored in, and how the saliency maps are pre-processed. In this paper, we provide an analysis of 8 different evaluation metrics and their properties. With the help of systematic experiments and visualizations of metric computations, we add interpretability to saliency scores and more transparency to the evaluation of saliency models. Building off the differences in metric properties and behaviors, we make recommendations for metric selections under specific assumptions and for specific applications.

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What Do Different Evaluation Metrics Tell Us About Saliency Models

Imaging techniques seek to simulate the array of light that reaches our eyes to provide the illusion of sensing scenes directly. Both photography and computer graphics deal with the generation of images. Both disciplines have to cope with the high dynamic range in the energy of visible light that human eyes can sense. Traditionally photography and computer graphics took different approaches to the high dynamic range problem. Work over the last ten years though has unified these disciplines and created powerful new tools for the creation of complex, compelling and realistic images. This book provides a practical introduction to the emerging new discipline of high dynamic range imaging that combines photography and computer graphics. By providing detailed equations and code, the book gives the reader the tools needed to experiment with new techniques for creating compelling images. A supplemental website contains downloads and additional information.

Advanced High Dynamic Range Imaging: Theory and Practice

In recent years many Tone Mapping Operators (TMOs) have been presented in order to display High Dynamic Range Images (HDRI) on typical display devices. TMOs compress the luminance range while trying to maintain contrast. The dual of tone mapping, inverse tone mapping, expands a Low Dynamic Range Image (LDRI) into a HDRI. HDRIs contain a broader range of physical values that can be perceived by the human visual system. The majority of today's media is stored in low dynamic range. Inverse Tone Mapping Operators (iTMOs) could thus potentially revive all of this content for use in high dynamic range display and image-based lighting. We propose an approximate solution to this problem that uses median-cut to find the areas considered of high luminance and subsequently apply a density estimation to generate an Expand-map in order to extend the range in the high luminance areas using an inverse Photographic Tone Reproduction operator.

Inverse tone mapping

High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end-to-end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.

/pdf/expandnet-a-deep-convolutional-neural-network-for-high-4lfoaoljnx.pdf

ExpandNet : a deep convolutional neural network for high dynamic range expansion from low dynamic range content

This book explores the methods needed for creating and manipulating HDR content. HDR is a step change from traditional imaging; more closely matching what we see with our eyes. In the years since the first edition of this book appeared, HDR has become much more widespread, moving from a research concept to a standard imaging method. This new edition incorporates all the many developments in HDR since the first edition and once again emphasizes practical tips, including the authors' popular HDR Toolbox (available on the authors' website) for MATLAB and gives readers the tools they need to develop and experiment with new techniques for creating compelling HDR content.
Key Features:


Contains the HDR Toolbox for readers' experimentation on authors' website


Offers an up-to-date, detailed guide to the theory and practice of high dynamic range imaging


Covers all aspects of the field, from capture to display


Provides benchmarks for evaluating HDR imagery

Advanced High Dynamic Range Imaging

The computation of high-fidelity images in real-time remains one of the key challenges for computer graphics Recent work has shown that by understanding the human visual system, selective rendering may be used to render only those parts to which the human viewer is attending at high quality and the rest of the scene at a much lower quality This can result in a significant reduction in computational time, without the viewer being aware of the quality difference Selective rendering is guided by models of the human visual system, typically in the form of a 2D saliency map, which predict where the user will be looking in any scene Computation of these maps themselves often take many seconds, thus precluding such an approach in any interactive system, where many frames need to be rendered per second In this paper we present a novel saliency map which exploits the computational performance of modern GPUs With our approach it is thus possible to calculate this map in milliseconds, allowing it to be part of a real time rendering system In addition, we also show how depth, habituation and motion can be added to the saliency map to further guide the selective rendering This ensures that only the most perceptually important parts of any animated sequence need be rendered in high quality The rest of the animation can be rendered at a significantly lower quality, and thus much lower computational cost, without the user being aware of this difference

Kurt Debattista

Papers

Advanced High Dynamic Range Imaging: Theory and Practice

Inverse tone mapping

ExpandNet : a deep convolutional neural network for high dynamic range expansion from low dynamic range content

Advanced High Dynamic Range Imaging

A GPU based saliency map for high-fidelity selective rendering