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.

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.

While real scenes produce a wide range of brightness variations, vision systems use low dynamic range image detectors that typically provide 8 bits of brightness data at each pixel. The resulting low quality images greatly limit what vision can accomplish today. This paper proposes a very simple method for significantly enhancing the dynamic range of virtually any imaging system. The basic principle is to simultaneously sample the spatial and exposure dimensions of image irradiance. One of several ways to achieve this is by placing an optical mask adjacent to a conventional image detector array. The mask has a pattern with spatially varying transmittance, thereby giving adjacent pixels on the detector different exposures to the scene. The captured image is mapped to a high dynamic

Abstract—Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image intensity of an imaging system is called the camera response. We analyze the properties that all camera responses share. This allows us to find the constraints that any response function must satisfy. These constraints determine the theoretical space of all possible camera responses. We have collected a diverse database of real-world camera response functions (DoRF). Using this database, we show that real-world responses occupy a small part of the theoretical space of all possible responses. We combine the constraints from our theoretical space with the data from DoRF to create a low-parameter empirical model of response (EMoR). This response model allows us to accurately interpolate the complete response function of a camera from a small number of measurements obtained using a standard chart. We also show that the model can be used to accurately estimate the camera response from images of an arbitrary scene taken using different exposures. The DoRF database and the EMoR model can be downloaded at

Many computer vision algorithms rely on precise estimates of scene radiances obtained from an image. A simple way to acquire a larger dynamic range of scene radiances is by combining several exposures of the scene. The number of exposures and their values have a dramatic impact on the quality of the combined image. At this point, there exists no principled method to determine these values. Given a camera with known response function and dynamic range, we wish to find the exposures that would result in a set of images that when combined would emulate an effective camera with a desired dynamic range and a desired response function. We first prove that simple summation combines all the information in the individual exposures without loss. We select the exposures by minimizing an objective function that is based on the derivative of the response function. Using our algorithm, we demonstrate the emulation of cameras with a variety of response functions, ranging from linear to logarithmic. We verify our method on several real scenes. Our method makes it possible to construct a table of optimal exposure values. This table can be easily incorporated into a digital camera so that a photographer can emulate a wide variety of high dynamic range cameras by selecting from a menu. 1 Capturing a Flexible Dynamic Range Many computer vision algorithms require accurate estimates of scene radiance such as color constancy [9], inverse rendering [13, 1] and shape recovery [17, 8, 18]. It is difficult to capture both the wide range of radiance values real scenes produce and the subtle variations within them using a low cost digital camera. This is because any camera must assign a limited number of brightness values to the entire range of scene radi-

This paper presents an approach for improving the effective dynamic range of cameras by using multiple photographs of the same scene taken with different exposure times. Using this method enables the photographer to accurately capture scenes that contain high dynamic range by using a device with low dynamic range. This allows the capture of scenes that have both very bright and very dark regions. The approach requires an initial camera calibration to determine the response function of the camera. Once the response function for a camera is known, high dynamic range images can be computed easily with only a small number of captured images. The high dynamic range output image consists of a weighted average of data from the multiply-exposed input images, and thus contains information captured by each of the input images. From a computational standpoint, the proposed algorithm is very efficient and requires little processing time to determine a solution. Keywords Dynamic range, multiframe...

High dynamic range (HDR) imaging has become a powerful tool in computer graphics, and is being applied to scenarios like simulation of different film responses, motion blur, and image-based illumination. The HDR images for these applications are typically generated by combining the information from multiple photographs taken at different exposure settings. Unfortunately, the color calibration of these images has so far been limited to very simplistic approaches such as a simple white balance algorithm. More sophisticated methods used for device-independent color representations are not easily applicable because they inherently assume a limited dynamic range. In this paper, we introduce a novel approach for constructing HDR images directly from low dynamic range images that were calibrated using an ICC input profile. 1.

The experienced quality of virtual reality is currently limited by available computational resources, and will be for some time to come. It is essential that these limited resources not be wasted on the acquisition and processing of data that do not contribute significantly to the final percept. Efficient construction of the intermediate views that are the basis of some forms of virtual reality (telepresence) depends on the proper selection of the acquired views. A fixed array of sensors that afford adequate resolution over the entire scene can present a prohibitive cost in bandwidth and computation while complete sensor mobility is technically difficult to achieve without becoming unacceptably intrusive. Active vision provides a mechanism of effective and efficient resource allocation in the transformation of real scenes into virtual ones. The use of electronic cameras and lenses mounted on positionable platforms (pan and tilt) that can track objects of interest, maintain sharp focus ...

This paper presents an approach for improving the effective dynamic range of cameras by using multiple photographs of the same scene taken with different exposure times. Using this method enables the photographer to accurately capture scenes that contain a high dynamic range, i.e., scenes that have both very bright and very dark regions. The approach requires an initial calibration, where the camera response function is determined. Once the response function for a camera is known, high dynamic range images can be computed easily. The high dynamic range output image consists of a weighted average of the multiply-exposed input images, and thus contains information captured by each of the input images. From a computational standpoint, the proposed algorithm is very efficient, and requires little processing time to determine a solution. 1. INTRODUCTION Intensity values of scenes in the real world can have a very broad dynamic range. This is particularly true for scenes that have areas of...

Abstract not found