This short note studies a variation of the Compressed Sensing paradigm introduced recently by Vaswani et al., i.e. the recovery of sparse signals from a certain number of linear m...
We present an approach to low-level vision that combines two main ideas: the use of convolutional networks as an image processing architecture and an unsupervised learning procedu...
We examine the problem of finding the optimal weight of the fidelity term in variational denoising. Our aim is to maximize the signal to noise ratio (SNR) of the restored image. ...
While noise is usually regarded as a problem of the image formation process, we observe that it is also frequently part of natural texture. In this paper, we present a concept for...
We propose a simple but flexible method for solving the generalized vector-valued TV (VTV) functional, which includes both the 2 -VTV and 1 -VTV regularizations as special cases, ...