A reality check for radiometric camera response recovery algorithms

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. [Display omitted] •Four camera response recovery algorithms commonly used in HDR imaging are compared.•Our comparison uses three metrics namely accuracy, consistency, and robustness.•Grossberg and Nayar's algorithm is found to be the most accurate.•Mitsunaga and Nayar's (MN) algorithm is found to be the most consistent.•Debevec and Malik's and MN algorithms are found to be the most robust against noise.