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

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

Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.

https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-23/issue-2/029901/Color-Appearance-Models/10.1117/1.JEI.23.2.029901.pdf

Color Appearance Models

This landmark book is the first to describe HDRI technology in its entirety and covers a wide-range of topics, from capture devices to tone reproduction and image-based lighting. The techniques described enable you to produce images that have a dynamic range much closer to that found in the real world, leading to an unparalleled visual experience. As both an introduction to the field and an authoritative technical reference, it is essential to anyone working with images, whether in computer graphics, film, video, photography, or lighting design.



New material includes chapters on High Dynamic Range Video Encoding, High Dynamic Range Image Encoding, and High Dynammic Range Display Devices
Written by the inventors and initial implementors of High Dynamic Range Imaging
Covers the basic concepts (including just enough about human vision to explain why HDR images are necessary), image capture, image encoding, file formats, display techniques, tone mapping for lower dynamic range display, and the use of HDR images and calculations in 3D rendering
Range and depth of coverage is good for the knowledgeable researcher as well as those who are just starting to learn about High Dynamic Range imaging


Table of Contents

Introduction; Light and Color; HDR Image Encodings; HDR Video Encodings; HDR Image and Video Capture; Display Devices; The Human Visual System and HDR Tone Mapping; Spatial Tone Reproduction; Frequency Domain and Gradient Domain Tone Reproduction; Inverse Tone Reproduction; Visible Difference Predictors; Image-Based Lighting.

High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics)

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data.

/pdf/light-field-image-processing-an-overview-3jq61afb1y.pdf

Light Field Image Processing: An Overview

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

The separation of reflection components is an important issue in computer graphics, computer vision and image processing. It provides useful information for the applications that need consistent object surface appearance, such as stereo reconstruction, visual recognition, tracking, objects re-illumination and dichromatic editing. In this paper we will present a brief survey of recent advances in separation of reflection components, also known as specularity (highlights) removal. Several techniques that try to tackle the problem from different points of view have been proposed so far. In this survey, we will overview these methods and we will present a critical analysis of their benefits and drawbacks.

/pdf/a-survey-of-specularity-removal-methods-3haa1ja8tu.pdf

A survey of specularity removal methods

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

In the last few years, researchers in the field of High Dynamic Range (HDR) Imaging have focused on providing tools for expanding Low Dynamic Range (LDR) content for the generation of HDR images due to the growing popularity of HDR in applications, such as photography and rendering via Image-Based Lighting, and the imminent arrival of HDR displays to the consumer market. LDR content expansion is required due to the lack of fast and reliable consumer level HDR capture for still images and videos. Furthermore, LDR content expansion, will allow the re-use of legacy LDR stills, videos and LDR applications created, over the last century and more, to be widely available. The use of certain LDR expansion methods, those that are based on the inversion of Tone Mapping Operators (TMOs), has made it possible to create novel compression algorithms that tackle the problem of the size of HDR content storage, which remains one of the major obstacles to be overcome for the adoption of HDR. These methods are used in conjunction with traditional LDR compression methods and can evolve accordingly. The goal of this report is to provide a comprehensive overview on HDR Imaging, and an in depth review on these emerging topics.

http://domino.mpi-inf.mpg.de/intranet/ag4/ag4publ.nsf/0/C54556685BC86D61C125755C005A9EBC/$file/HDRIandLDRExpansion.pdf

Francesco Banterle

Papers

Advanced High Dynamic Range Imaging: Theory and Practice

Inverse tone mapping

A survey of specularity removal methods

Advanced High Dynamic Range Imaging

High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content