Developing Nonstationary Noise Estimation for Application in Edge and Corner Detection

Authors:
P. Wyatt;H. Nakai
Affiliations:
Toshiba Res. Lab., Kawasaki;-
Venue:
IEEE Transactions on Image Processing
Year:
2007

Citing 0
Cited 2

Robust Candidate Pruning Approach Based on the PSO-SVM for Fast Corner Detection with Noise Tolerance in Gray-Level Images

Fundamenta Informaticae - Swarm Intelligence
Robust Candidate Pruning Approach Based on the PSO-SVM for Fast Corner Detection with Noise Tolerance in Gray-Level Images

Fundamenta Informaticae - Swarm Intelligence

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Abstract

Accurate estimation of noise and signal power is of fundamental interest in a wide variety of vision applications as it is critical to thresholding and decision processes. This paper proposes two methods for the estimation of nonstationary noise based upon models of image structure which locally separate signal from noise. The resulting algorithms are noniterative and thereby fast. The accuracy of the proposed and existing methods is compared, first separately and then in application to two common image processing tasks: edge and corner detection. It is demonstrated that the proposed model can be used to improve the stability of both, in the presence of contrast change and nonstationary noise.