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.

An important objective in the evaluation of algorithms with sensory inputs is the development of measures characterizing the intrinsic errors in the results. Intrinsic are those errors which are caused by noise in the input data. The particular application which we consider is 3-D reconstruction from stereo. We demonstrate that a radiometric correction of the images could improve signi cantly the accuracy. We propose a con dence interval approach for quantifying the precision. We also illustrate the use of the con dence intervals for the rejection of unreliable 3D points.

Abstract—Advances in CMOS image sensors enable high-speed image readout, which makes it possible to capture multiple images within a normal exposure time. Earlier work has demonstrated the use of this capability to enhance sensor dynamic range. This paper presents an algorithm for synthesizing a high dynamic range, motion blur free, still image from multiple captures. The algorithm consists of two main procedures, photocurrent estimation and saturation and motion detection. Estimation is used to reduce read noise, and, thus, to enhance dynamic range at the low illumination end. Saturation detection is used to enhance dynamic range at the high illumination end as previously proposed, while motion blur detection ensures that the estimation is not corrupted by motion. Motion blur detection also makes it possible to extend exposure time and to capture more images, which can be used to further enhance dynamic range at the low illumination end. Our algorithm operates completely locally; each pixel’s final value is computed using only its captured values, and recursively, requiring the storage of only a constant number of values per pixel independent of the number of images captured. Simulation and experimental results demonstrate the enhanced signal-to-noise ratio (SNR), dynamic range, and the motion blur prevention achieved using the algorithm. Index Terms—CMOS image sensor, dynamic range extension, motion blur restoration, motion detection, photocurrent estimation, saturation detection. I.

We present a virtual digital camera sensor, whose aim is to simulate a real (physical) image capturing sensor. To accomplish this task, the virtual sensor operates in two steps. First, it accepts a physical description of a given scene and simulates the entire process of photon sensing and charge generation in the sensor device. This process is affected by noise, mostly photon noise. Second, it adds to the image the noise that results from the electronic circuitry. We present a model for the different sources of noise relative to each sensor-based image formation step, and use measurements of real digital camera images to validate the model. Keywords: Digital cameras, noise, CCD, CMOS. 1.

Abstract. An original concept for computing instantaneous 3D pose and 3D velocity of fast moving objects using a single view is proposed, implemented and validated. It takes advantage of the image deformations induced by rolling shutter in CMOS image sensors. First of all, after analysing the rolling shutter phenomenon, we introduce an original model of the image formation when using such a camera, based on a general model of moving rigid sets of 3D points. Using 2D-3D point correspondences, we derive two complementary methods, compensating for the rolling shutter deformations to deliver an accurate 3D pose and exploiting them to also estimate the full 3D velocity. The first solution is a general one based on non-linear optimization and bundle adjustment, usable for any object, while the second one is a closed-form linear solution valid for planar objects. The resulting algorithms enable us to transform a CMOS low cost and low power camera into an innovative and powerful velocity sensor. Finally, experimental results with real data confirm the relevance and accuracy of the approach. 1

We present preliminary results confirming the importance of radiometric correction in multicamera applications. Although, in this paper we compensate for systematic noise only, we review all noise sources in the video sensor (systematic and random). We use a simple model for radiometric correction of digital images. The correction procedure is tested on the disparity map computation in stereo matching, particularly in a case where stereo usually fails --- almost textureless white surface. Without correcting radiometricly, the matching algorithm matches systematic noise components in the two images. With the correction, after removing the systematic noise, an improvement of 26% to 59% in relative rms of the disparity map is demonstrated (the higher the intensity of the flat field, the better the improvement). 1 Introduction When multiple sensors are used in applications requiring hard performance guarantees, correcting for errors and obtaining objective confidence measures for the unce...

The purpose of this report is to help computer vision researchers to understand the video sensor data, and hence, utilize better the data in vision algorithms, and also evaluate correctly and methodically the results of the algorithms. The vision sensors are complex electronic systems and as such exhibit systematic and random errors. Unfortunately important specifications regarding cameras are not standardly and unambiguously provided by manufacturers. Such specifications are necessary for some scientific and engineering applications. In this report we present the major components of the imaging system, and give the main parameters and noise sources. There is no agreement in the literature (and manufacturers' documentation) on the nomenclature, definitions, measurement units and/or the conditions under which these parameters and noise levels are measured. As a general rule, video cameras are for qualitative imaging (this does not apply for scientific and special purpose camer...

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We present a virtual digital camera sensor, whose aim is to simulate a real (physical) image capturing sensor. To accomplish this task, the virtual sensor operates in two steps. First, it accepts a physical description of a given scene and simulates the entire process of photon sensing and charge generation in the sensor device. This process is a#ected by noise, mostly photon noise. Second, it adds to the image the noise that results from the electronic circuitry. We present a model for the di#erent sources of noise relative to each sensor-based image formation step, and use measurements of real digital camera images to validate the model.

Correction of systematic spatial noise in push-broom