منابع مشابه
Denoiser-loss estimators and twice-universal denoising
We study the concentration of denoiser loss estimators, with application to the selection of denoiser parameters for a given observed sequence (in particular, the window size k of the DUDE algorithm [1]) via minimization of the estimated loss. We show that for a loss estimator proposed earlier [2], it is not possible to derive strong concentration results for certain pathological input sequence...
متن کاملTwice Universal Linear Prediction of Individual Sequences
We present a linear prediction algorithm which is \twice universal," over parameters and model orders, for individual sequences under the square-error loss function. The sequentially accumulated mean-square prediction error is as good as any linear predictor of order up to some M. Following an approach taken in many prediction problems we transform the linear prediction problem into a sequentia...
متن کاملApproximate Message Passing with Universal Denoising
We study compressed sensing (CS) signal reconstruction problems where an input signal is measured via matrix multiplication under additive white Gaussian noise. Our signals are assumed to be stationary and ergodic, but the input statistics are unknown; the goal is to provide reconstruction algorithms that are universal to the input statistics. We present a novel algorithmic framework that combi...
متن کاملUniversal Denoising Networks : A Novel CNN-based Network Architecture for Image Denoising
We design a novel network architecture for learning discriminative image models that are employed to efficiently tackle the problem of grayscale and color image denoising. Based on the proposed architecture, we introduce two different variants. The first network involves convolutional layers as a core component, while the second one relies instead on non-local filtering layers and thus it is ab...
متن کاملCompressed Sensing via Universal Denoising and Approximate Message Passing
We study compressed sensing (CS) signal reconstruction problems where an input signal is measured via matrix multiplication under additive white Gaussian noise. Our signals are assumed to be stationary and ergodic, but the input statistics are unknown; the goal is to provide reconstruction algorithms that are universal to the input statistics. We present a novel algorithm that combines: (i) the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2013
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2012.2216503