While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this paper, we introduce a novel model, the Robust Boltzmann Machine (RoBM), w
BLS-GSM代表“Bayesian Least Squares - Gaussian Scale Mixture(贝叶斯最小二乘-高斯尺度混合模型)”。 这个工具箱实现了该篇论文中介绍的算法： J Portilla, V Strela, M Wainwright, E P Simoncelli, Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain, IEEE Transactions on Image
Non-local means算法由A. Buades, B. Coll, J.M Morel提出。该算法的主要参考文献为： A. Buades, B. Coll, J.M Morel, A review of image denoising algorithms, with a new one , Multiscale Modeling and Simulation (SIAM interdisciplinary journal), Vol 4 (2), pp: 490-530, 2005.
一种降噪算法 Abstract—We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictio- narie
We consider the problem of the joint denoising of a number of raw- data images from a digital imaging sensor. In particular, we exploit a recently proposed image modeling [8] that incorporates both the signal-dependent nature of noise and the clippi
Most real-world recommender services measure their performance based on the top-N results shown to the end users. Thus, advances in top-N recommendation have far-ranging consequences in practical applications. In this paper, we present a novel metho
