Self-organized operational neural networks for severe image restoration problems

Self-organized critical neural networks.

A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks,...

Bayesian Neural Networks for Image Restoration

Numerical methods commonly employed to convert experimental data into interpretable images and spectra commonly rely on straightforward transforms, such as the Fourier transform (FT), or quite elaborated emerging classes of transforms, like wavelets (Meyer, 1993; Mallat, 2000), wedgelets (Donoho, 1996), ridgelets (Candes, 1998), and so forth. Yet experimental data are incomplete and noisy due t...

Self-organized criticality in adaptive neural networks

Efficient image restoration using cellular neural networks

In this paper, a 3-D Cellular Neural Network (CNN) is applied for restoration of degraded images. It is known that regularized or Maximum a Posteriori estimation based image restoration problems can be formulated as the minimization of the Lyapunov function of the discrete-time Hopeld network. Recently, this Lyapunov function based design method has been extended to the continuous-time Hopeld n...

Self-Commmittee Approach for Image Restoration Problems using Convolutional Neural Network

There have been many discriminative learning methods using convolutional neural networks (CNN) for several image restoration problems, which learn the mapping function from a degraded input to the clean output. In this letter, we propose a self-committee method that can find enhanced restoration results from the multiple trial of a trained CNN with different but related inputs. Specifically, it...