Shape from shading
The RADIANCE lighting simulation and rendering system
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Video and HDTV Algorithms and Interfaces
Digital Video and HDTV Algorithms and Interfaces
Modeling the Space of Camera Response Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Radiometric Response Function from a Single Grayscale Image
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Veiling glare in high dynamic range imaging
ACM SIGGRAPH 2007 papers
The Frankencamera: an experimental platform for computational photography
ACM SIGGRAPH 2010 papers
Radiometric calibration from a single image
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
RGB calibration for color image analysis in machine vision
IEEE Transactions on Image Processing
Comparametric equations with practical applications in quantigraphic image processing
IEEE Transactions on Image Processing
Blind inverse gamma correction
IEEE Transactions on Image Processing
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The radiometric response of a camera governs the relationship between the incident light on the camera sensor and the output pixel values that are produced. This relationship, which is typically unknown and nonlinear, needs to be estimated for applications that require accurate measurement of scene radiance. Until now, various camera response recovery algorithms have been proposed each with different merits and drawbacks. However, an evaluation study that compares these algorithms has not been presented. In this work, we aim to fill this gap by conducting a rigorous experiment that evaluates the selected algorithms with respect to three metrics: consistency, accuracy, and robustness. In particular, we seek the answer of the following four questions: (1) Which camera response recovery algorithm gives the most accurate results? (2) Which algorithm produces the camera response most consistently for different scenes? (3) Which algorithm performs better under varying degrees of noise? (4) Does the sRGB assumption hold in practice? Our findings indicate that Grossberg and Nayar's (GN) algorithm (2004 [1]) is the most accurate; Mitsunaga and Nayar's (MN) algorithm (1999 [2]) is the most consistent; and Debevec and Malik's (DM) algorithm (1997 [3]) is the most resistant to noise together with MN. We also find that the studied algorithms are not statistically better than each other in terms of accuracy although all of them statistically outperform the sRGB assumption. By answering these questions, we aim to help the researchers and practitioners in the high dynamic range (HDR) imaging community to make better choices when choosing an algorithm for camera response recovery.