Image Noise Estimation Using A Variation-Adaptive Evolutionary Approach

The estimation of noise statistics is critical to optimize many computer vision algorithms. The main issue is how to identify the homogeneous image patches for estimating the noise statistics. The smallest variance used in the conventional approaches is not always a good measure of homogeneity of image patches. In addition, the conventional approaches neglect the fact that homogeneous image patches tend to cluster together due to local spatial smoothness in images. In view of this, a new image noise estimation approach is proposed in this letter. The proposed approach has two key components. First, a graphical representation is proposed to model the relationship among image patches. Second, the ant colony optimization (ACO) technique is used to automatically select a set of patches for estimating the noise statistics. To be more specific, the proposed approach guides the spatial movement of artificial ants towards homogeneous locations in the graph, by considering both global (i.e., clustering measure) properties and local (i.e., homogeneity measure) properties of patches. Experimental results are provided to justify that the proposed approach out-performs nine conventional approaches to provide more accurate noise statistics estimation.