In low-light conditions, it is known that Poisson noise and quantization noise become dominant sources of noise. While intensity difference is usually measured by Euclidean distance, it often breaks down due to an unnegligible amount of uncertainty in observations caused by noise. In this paper, we develop a new noise model based upon Poisson noise and quantization noise. We then propose a new intensity similarity function built upon the proposed noise model. The similarity measure is derived by maximum likelihood estimation based on the nature of Poisson noise and quantization process in digital imaging systems, and it deals with the uncertainty embedded in observations. The proposed intensity similarity measure is useful in many computer vision applications which involve intensity differencing, e.g., block matching, optical flow, and image alignment. We verified the correctness of the proposed noise model by comparisons with real-world noise data and confirmed superior robustness of the proposed similarity measure compared with the standard Euclidean norm.<\/p>\n","protected":false},"excerpt":{"rendered":"
In low-light conditions, it is known that Poisson noise and quantization noise become dominant sources of noise. While intensity difference is usually measured by Euclidean distance, it often breaks down due to an unnegligible amount of uncertainty in observations caused by noise. 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