Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Behavioral analysis of anisotropic diffusion in image processing
IEEE Transactions on Image Processing
The digital TV filter and nonlinear denoising
IEEE Transactions on Image Processing
On the origin of the bilateral filter and ways to improve it
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
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Noise removal from an observed signal is an important problem in signal processing. PDE-based methods have been widely used because of their common property being good at removing the noise while preserving the edges. These methods restore images by modifying towards cartoon-like images. Therefore, other important features such as textures and certain details tend to be degraded in the denoising process. In this work, we propose a modified variational denoising algorithm that adaptively selects the parameters according to the local nature of the image. In order to estimate the locally appropriate parameters, neural network based learning is employed. This method can adaptively change the denoising level by changing the regularization parameter and the smoothing kernel according to the local image. The results of denoising by the proposed method show both visual and objective improvements.