An MLP neural net with L1 and L2 regularizers for real conditions of deblurring

Authors:
Miguel A. Santiago;Guillermo Cisneros;Emiliano Bernués
Affiliations:
Departamento de Señales, Sistemas y Radiocomunicaciones, Escuela Téchica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain;Departamento de Señales, Sistemas y Radiocomunicaciones, Escuela Téchica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain;Departamento de Ingeniería Electrónica y Comunicaciones, Centro Politécnico Superior, Universidad de Zaragoza, Zaragoza, Spain
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2010

Citing 19
Cited 0

Nonlinear total variation based noise removal algorithms

Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Neural network design

Neural network design
A new iterative Monte Carlo approach for inverse matrix problem

Journal of Computational and Applied Mathematics
Circulant and aperiodic models of deconvolutions: a comparison

Signal Processing
A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions

SIAM Journal on Scientific Computing
Computational Methods for Inverse Problems

Computational Methods for Inverse Problems
Handbook of Image and Video Processing (Communications, Networking and Multimedia)

Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods

Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Iterative desensitisation of image restoration filters under wrong PSF and noise estimates

EURASIP Journal on Applied Signal Processing
A non-local regularization strategy for image deconvolution

Pattern Recognition Letters
Non-local Regularization of Inverse Problems

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Review: Adaptive total variation image deblurring: A majorization-minimization approach

Signal Processing
Image restoration using hopfield neural network based on total variational model

ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Hopfield neural network based algorithms for image restoration andreconstruction. II. Performance analysis

IEEE Transactions on Signal Processing
Bayesian and regularization methods for hyperparameter estimation in image restoration

IEEE Transactions on Image Processing
Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation

IEEE Transactions on Image Processing
Weight assignment for adaptive image restoration by neural networks

IEEE Transactions on Neural Networks
A neural learning approach for adaptive image restoration using a fuzzy model-based network architecture

IEEE Transactions on Neural Networks
Digital Image Restoration

Digital Image Restoration

Quantified Score

Hi-index	0.00

Visualization

Abstract

Real conditions of deblurring involve a spatially nonlinear process since the borders are truncated, causing significant artifacts in the restored results. Typically, it is assumed to have boundary conditions to reduce ringing; in contrast, this paper proposes a restoration method which simply deals with null borders. We minimize a deterministic regularized function in a Multilayer Perceptron (MLP) with no training and follow a back-propagation algorithm with the L1 and L2 norm-based regularizers. As a result, the truncated borders are regenerated while adapting the center of the image to the optimum linear solution. We report experimental results showing the good performance of our approach in a real model without borders. Even if using boundary conditions, the quality of restoration is comparable to other recent researches.