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Noise incursion is an inherent problem in dictionary training on noisy samples. Therefore, enforcing a structural constrain on the dictionary will be useful for a stable dictionary training. Recently, a sparse dictionary with predefined sparsity has been proposed as a structural constraint. However, a fixed sparsity can become too rigid to adapt to the training samples. In order to address this issue,...
Existing image denoising frameworks via sparse representation using learned dictionaries have an weakness that the dictionary, trained from noisy image, suffers from noise incursion. This paper analyzes this noise incursion, explicitly derives the noise component in the dictionary update step, and provides a simple remedy for a desired signal to noise ratio. The remedy is shown to perform better both...
Sparse representation using trained dictionary is advantageous over the standard parametric bases. Recently, a dictionary training algorithm called SGK has been proposed as an alternative to the well known K-SVD algorithm. Analytically it has been shown that SGK has a superior execution speed in comparison to K-SVD, and it is advantageous to use SGK for constrained sparse coding. Through synthetic...
We have recently proposed a Sequential Generalization of K-means (SGK) to train dictionary for sparse representation. SGK's training performance is as effective as the standard dictionary training algorithm K-SVD, alongside it has a simpler implementation to its advantage. In this piece of work, through the problem of image denoising, we are making a fair comparison between the usability of SGK and...
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