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Erschienen in:
2017 | OriginalPaper | Buchkapitel
A Variational Model to Extract Texture from Noisy Image Data with Local Variance Constraints
verfasst von : Tao Zhang, Qiuli Gao
Erschienen in: Image and Graphics
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Abstract
Variational image denoising is one of the most successful methods to recover an image that has been blurred and corrupted with additive noise. However, the Lagrange multiplier of many variational model is global, which leads to the phenomenon that some image regions get satisfactory restoration while the others fail. To avoid this, we propose an image denoising model including a set of local constraints (Lagrange multipliers), each one corresponding to a dyadic region of the image. Thus, the proposed model can denoise the image according to different types of the image region. The model is solved by the gradient descend based algorithm and performs fast. Then we propose a hybrid image denoising scheme combining the state of the art model and the proposed model. The experimental results demonstrate that the proposed method ensures better restoration quality.