We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equipment. In our method, multiple photographs of the scene are taken with different amounts of exposure. Our algorithm uses these differently exposed photographs to recover the response function of the imaging process, up to factor of scale, using the assumption of reciprocity. With the known response function, the algorithm can fuse the multiple photographs into a single, high dynamic range radiance map whose pixel values are proportional to the true radiance values in the scene. We demonstrate our method on images acquired with both photochemical and digital imaging processes. We discuss how this work is applicable in many areas of computer graphics involving digitized photographs, including image-based modeling, image compositing, and image processing. Lastly, we demonstrate a few applications of having high dynamic range radiance maps, such as synthesizing realistic motion blur and simulating the response of the human visual system.

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.

Abstract—A common task in computer graphics is the mapping of digital high dynamic range images to low dynamic range display devices such as monitors and printers. This task is similar to the adaptation processes which occur in the human visual system. Physiological evidence suggests that adaptation already occurs in the photoreceptors, leading to a straightforward model that can be easily adapted for tone reproduction. The result is a fast and practical algorithm for general use with intuitive user parameters that control intensity, contrast, and level of chromatic adaptation, respectively. Index Terms—Tone reproduction, dynamic range reduction, photoreceptor physiology. 1

Abstract- This paper proposes an approach for pixel transformation of the displayed image to increase the potential energy saving of the backlight scaling method. The proposed approach takes advantage of human visual system characteristics and tries to minimize distortion between the perceived brightness values of the individual pixels in the original image and those of the backlight-scaled image. This is in contrast to previous backlight scaling approaches which simply match the luminance values of the individual pixels in the original and backlight-scaled images. Moreover, the proposed dynamic backlight scaling approach, which is based on tone mapping, is amenable to highly efficient hardware realization because it does not need information about the histogram of the displayed image. Experimental results show that the dynamic tone mapping for backlight scaling method results in about 35 % power saving with an effective distortion rate of 5 % and 55 % power saving for a 20 % distortion rate.

The development of high dynamic range (HDR) imagery has brought us to the verge of arguably the largest change in image display technologies since the transition from black-and-white to color television. Novel capture and display hardware will soon enable consumers to enjoy the HDR experience in their own homes. The question remains, however, of what to do with existing images and movies, which are intrinsically low dynamic range (LDR). Can this enormous volume of legacy content also be displayed effectively on HDR displays? We have carried out a series of rigorous psychophysical investigations to determine how LDR images are best displayed on a state-of-the-art HDR monitor, and to identify which stages of the HDR imaging pipeline are perceptually most critical. Our main findings are: (1) As expected, HDR displays outperform LDR ones. (2) Surprisingly, HDR images that are tonemapped for display on standard monitors are often no better than the best single LDR exposure from a bracketed sequence. (3) Most importantly of all, LDR data does not necessarily require sophisticated treatment to produce a compelling HDR experience. Simply boosting the range of an LDR image linearly to fit the HDR display can equal or even surpass the appearance of a true HDR image. Thus the potentially tricky process of inverse tone mapping can be largely circumvented.

In this paper, we present a learning-based image processing technique. We have developed a novel method to map high dynamic range scenes to low dynamic range images for display in standard (low dynamic range) reproduction media. We formulate the problem as a quantization process and employ an adaptive conscience learning strategy to ensure that the mapped low dynamic range displays not only faithfully reproduce the visual features of the original scenes, but also make full use of the available display levels. This is achieved by the use of a competitive learning neural network that employs a frequency sensitive competitive learning mechanism to adaptively design the quantizer. By optimizing an L2 distortion function, we ensure that the mapped low dynamic images preserve the visual characteristics of the original scenes. By incorporating a frequency sensitive competitive mechanism, we facilitate the full utilization of the limited displayable levels. We have developed a deterministic and practicable learning procedure which uses a single variable to control the display result. We give a detailed description of the implementation procedure of the new learning-based high dynamic range compression method and present experimental results to demonstrate the effectiveness of the method in displaying a variety of high dynamic range scenes.

We propose a general hybrid approach to the issue of reproduction of high dynamic range images on devices with limited dynamic range. Our approach is based on combination of arbitrary global and local tone mapping operators. Recent perceptual studies concerning the reproduction of HDR images have shown high importance of preservation of overall image attributes. Motivated by these findings, we apply the global method first to reproduce overall image attributes correctly. At the same time, an enhancement map is constructed to guide a local operator to the critical areas that deserve enhancement. Based on the choice of involved methods and on the manner of construction of an enhancement map, we show that our approach is general and can be easily tailored to miscellaneous goals of tone mapping. An implementation of proposed hybrid tone mapping produces good results, it is easy to implement, fast to compute and it is comfortably scalable, if desired. These qualities nominate our approach for utilization in time-critical HDR applications like interactive visualizations, modern computer games, HDR image viewers, HDR mobile devices applications, etc.

In film production, it is sometimes not convenient or directly impossible to shoot some night scenes at night. The film budget, schedule or location may not allow it. In these cases, the scenes are shot at daytime, and the ’night look ’ is achieved by placing a blue filter in front of the lens and under-exposing the film. This technique, that the American film industry has used for many decades, is called ’Day for Night ’ (or ’American Night ’ in Europe.) But the images thus obtained don’t usually look realistic: they tend to be too bluish, and the objects ’ brightness seems unnatural for night-light. In this article we introduce a digital Day for Night algorithm that achieves very realistic results. We use a set of very simple equations, based on real physical data and visual perception experimental data. To simulate the loss of visual acuity we introduce a novel diffusion Partial Differential Equation (PDE) which takes luminance into account and respects contrast, produces no ringing, is stable, very easy to implement and fast. The user only provides the original day image and the desired level of darkness of the result. The whole process from original day image to final night image is implemented in a few seconds, computations being mostly local. Keywords: Day for night, visual perception, dark adaptation, Weber’s Law, non-linear diffusion 1.

With interest in high dynamic range imaging mounting, techniques for displaying such images on conventional display devices are gaining in importance. Conversely, high dynamic range display hardware is creating the need for display algorithms that prepare images for such displays. In this paper, the current state-of-the-art in dynamic range reduction and expansion is reviewed, and in particular we assess the theoretical and practical need to structure tone reproduction as a combination of a forward and a reverse pass. 1

This document serves as course notes. It is intended to complement the RADIANCE original documentation giving more details in some essential areas. Technical terms used within this document come mainly from the lighting domain [CIE87] or the computer graphics domain [Jem92]; the remaining terms are part of the specific jargon employed in the RADIANCE original documentation