On the Suitable Domain for SVM Training in Image Coding
The Journal of Machine Learning Research
Context-based defading of archive photographs
Journal on Image and Video Processing - Special issue on image and video processing for cultural heritage
Image Denoising with Kernels Based on Natural Image Relations
The Journal of Machine Learning Research
Counter-examples for Bayesian MAP restoration
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Digital removal of blotches with variable semi-transparency using visibility laws
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Visual cortex performs a sort of non-linear ICA
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
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Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density. Simple statistical models for spectral estimation just take into account second-order relations between the pixels of the image. However, natural images exhibit additional features, such as particular relationships between local Fourier or wavelet transform coefficients. Biological visual systems have evolved to capture these relations. We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models. The results suggest that if the penalty operator takes these additional features in natural images into account, it will be more robust and the choice of the regularization parameter is less critical.