Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content

Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, usually the choice of such parameters is made empirically with trial and error if no "ground-truth" reference is available. Some analytical methods such as cross-validation and Stein's unbiased risk estimate (SURE) have been successfully used to set such parameters. However, these methods tend to be strongly reliant on restrictive assumptions on the noise, and also computationally heavy. In this paper, we propose a no-reference metric Q which is based upon singular value decomposition of local image gradient matrix, and provides a quantitative measure of true image content (i.e., sharpness and contrast as manifested in visually salient geometric features such as edges,) in the presence of noise and other disturbances. This measure 1) is easy to compute, 2) reacts reasonably to both blur and ran...

• Type: Journal
• Year: 2010
• Where: TIP
• Authors: Xiang Zhu, Peyman Milanfar
• Keywords: Education, Image, Parameters, TIP 2010, Unbiased Risk Estimate