Image Restoration using Multilayer Neural Networks with Minimization of Total Variation Approach (original) (raw)

Weight assignment for adaptive image restoration by neural networks

IEEE Transactions on Neural Networks, 2000

This paper presents a scheme for adaptively training the weights, in terms of varying the regularization parameter, in a neural network for the restoration of digital images. The flexibility of neural-network-based image restoration algorithms easily allow the variation of restoration parameters such as blur statistics and regularization value spatially and temporally within the image. This paper focuses on spatial variation of the regularization parameter. We first show that the previously proposed neural-network method based on gradient descent can only find suboptimal solutions, and then introduce a regional processing approach based on local statistics. A method is presented to vary the regularization parameter spatially. This method is applied to a number of images degraded by various levels of noise, and the results are examined. The method is also applied to an image degraded by spatially variant blur. In all cases, the proposed method provides visually satisfactory results in an efficient way.

Image Restoration in Neural Network Domain using Back Propagation Network Approach

Image, 2011

Image Restoration is a process by which an image suffering some form of distortion or degradation can be recovered to its original form. Many techniques have been implemented for image restoration for achieving better performance and quality image, often the benefits of improving image quality to the maximum possible extent far outweigh the cost and complexity of the restoration algorithms involved. In this paper, we consider the problem of an image restoration degraded by a blur function and corrupted by noise. Here we are applying the Back Propagation Neural Network approach for image restoration. This method is an iterative approach and attractive because of its improved performance and achieving high quality image in terms of peak signal to noise ratio.

Image restoration using L1-norm regularization and a gradient-based neural network with discontinuous activation functions

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008

The problem of restoring images degraded by linear position invariant distortions and noise is solved by means of a L1-norm regularization, which is equivalent to determining a L1norm solution of an overdetermined system of linear equations, which results from a data-fitting term plus a regularization term that are both in L1 norm. This system is solved by means of a gradient-based neural network with a discontinuous activation function, which is ensured to converge to a L1-norm solution of the corresponding system of linear equations.

Loss Functions for Image Restoration With Neural Networks

IEEE Transactions on Computational Imaging, 2017

Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is 2. In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.

A Novel Approach for Medical Images Noise Reduction Using Neural Filter

This paper is dedicated to the presentation of a new denoising method for medical images. In the proposed approach, a neural filter is designed based on total variation regularizati on using a multilayer neural network (MLP) to reduce the noise from the degraded images. To train the network we use optimization techniques, which require the calculation of the gradient of the error to adjust the weights by the minimization of an appropriate error function. The new approach is based on taking profit from local information (neighborhood) of the pixel to denoise it. The proposed method can restore degraded images and preserves the discontinuities. The considered filter was used to reduce noise from X-ray and MRI medical images giving good results when compared to other approaches.

Functional Neural Networks for Parametric Image Restoration Problems

arXiv (Cornell University), 2021

Almost every single image restoration problem has a closely related parameter, such as the scale factor in super-resolution, the noise level in image denoising, and the quality factor in JPEG deblocking. Although recent studies on image restoration problems have achieved great success due to the development of deep neural networks, they handle the parameter involved in an unsophisticated way. Most previous researchers either treat problems with different parameter levels as independent tasks, and train a specific model for each parameter level; or simply ignore the parameter, and train a single model for all parameter levels. The two popular approaches have their own shortcomings. The former is inefficient in computing and the latter is ineffective in performance. In this work, we propose a novel system called functional neural network (FuncNet) to solve a parametric image restoration problem with a single model. Unlike a plain neural network, the smallest conceptual element of our FuncNet is no longer a floating-point variable, but a function of the parameter of the problem. This feature makes it both efficient and effective for a parametric problem. We apply FuncNet to superresolution, image denoising, and JPEG deblocking. The experimental results show the superiority of our FuncNet on all three parametric image restoration tasks over the state of the arts.

An adaptive algorithm for restoring image corrupted by mixed noise

Cybernetics and Physics

Image denoising is one of the fundamental problems in image processing. Digital images are often contaminated by noise due to the image acquisition process under poor conditions. In this paper, we propose an effective approach to remove mixed Poisson-Gaussian noise in digital images. Particularly, we propose to use a spatially adaptive total variation regularization term in order to enhance the ability of edge preservation. We also propose an instance of the alternating direction algorithm to solve the proposed denoising model as an optimization problem. The experiments on popular natural images demonstrate that our approach achieves superior accuracy than other recent state-of-the-art techniques.

Real-Time Image Restoration with an Artificial Neural Network

. We present a neural network that can be applied to image correction in a preprocessing unit. Blur, geometric distortion and unequal brightness distribution are typical for many scanning techniques and can lead to difficulties during further processing of an image. These and other effects of image degradation which frequently appear spacevariant can be considered simultaneously by this approach. In order to calibrate the correcting system the weights of a neural network are trained. Using suitable training patterns and an appropriate optimization criterion for the degraded images, in the result the dimensioned network represents a space-variant filter with a behavior similar to the well-known Wiener filter. The restoration result can be easily altered by the scheme of the learning data generation. Theoretical considerations and examples for 1-D, 2-D and 3-D implementations in soft- and hardware are given. 1. Introduction When operating with image signals, irregularities of the scan...

Total variation image restoration: numerical methods and extensions

Proceedings of International Conference on Image Processing, 1997

We describe some numerical techniques for the Total Variation image restoration method, namely a primaldual linearization for the Euler-Lagrange equations and some preconditioning issues. We also highlight extension of this technique to color images, blind deconvolution and the staircasing e ect.