Real-Time Image Restoration with an Artificial Neural Network (original) (raw)
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An artificial neural network for real-time image restoration
Today optical measuring devices are used in many applications. The measurement accuracy should be very good. But when operating with image signals, irregularities of the scanning system must often be corrected. Blur, geometric distortion and unequal brightness distribution can lead to difficulties during further processing of an image. In the following, it is shown how an artificial neural network can be applied to image restoration. In order to calibrate the correcting system the weights of the 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. A pipeline processor simulates a neural network operating in real time. Theoretical considerations and experimental results are given in this this paper
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.
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.
Color Image Restoration Using Neural Network Model
2011
Neural network learning approach for color image restoration has been discussed in this paper and one of the possible solutions for restoring images has been presented. Here neural network weights are considered as regularization parameter values instead of explicitly specifying them. The weights are modified during the training through the supply of training set data. The desired response of the network is in the form of estimated value of the current pixel. This estimated value is used to modify the network weights such that the restored value produced by the network for a pixel is as close as to this desired response. One of the advantages of the proposed approach is that, once the neural network is trained, images can be restored without having prior information about the model of noise/blurring with which the image is corrupted.
IEEE Transactions on Neural Networks, 2001
In this paper, we address the problem of adaptive regularization in image restoration by adopting a neural-network learning approach. Instead of explicitly specifying the local regularization parameter values, they are regarded as network weights which are then modified through the supply of appropriate training examples. The desired response of the network is in the form of a gray level value estimate of the current pixel using weighted order statistic (WOS) filter. However, instead of replacing the previous value with this estimate, this is used to modify the network weights, or equivalently, the regularization parameters such that the restored gray level value produced by the network is closer to this desired response. In this way, the single WOS estimation scheme can allow appropriate parameter values to emerge under different noise conditions, rather than requiring their explicit selection in each occasion. In addition, we also consider the separate regularization of edges and textures due to their different noise masking capabilities. This in turn requires discriminating between these two feature types. Due to the inability of conventional local variance measures to distinguish these two high variance features, we propose the new edge-texture characterization (ETC) measure which performs this discrimination based on a scalar value only. This is then incorporated into a fuzzified form of the previous neural network which determines the degree of membership of each high variance pixel in two fuzzy sets, the EDGE and TEXTURE fuzzy sets, from the local ETC value, and then evaluates the appropriate regularization parameter by appropriately combining these two membership function values.
Parametric signal restoration using artificial neural networks
IEEE Transactions on Biomedical Engineering, 1996
The problem of parametric signal restoration given its blurredhonlinearly distorted version contaminated by additive noise is discussed. It is postulated that feedforward artificial neural networks can be used to find a solution to this problem. The proposed estimator does not require iterative calculations that are normally performed using numerical methods for signal parameter estimation. Thus high speed is the main advantage of this approach. A two-stage neural network-based estimator architecture is considered in which the vector of measurements is projected on the signal subspace and the resulting features form the input to a feedforward neural network. The effect of noise on the estimator performance is analyzed and compared to the least-squares technique. It is shown, for low and moderate noise levels, that the two estimators are similar to each other in terms of their noise performance, provided the neural network approximates the inverse mapping from the measurement space to the parameter space with a negligible error. However, if the neural network is trained on noisy signal observations, the proposed technique is superior to the least-squares estimate (LSE) model fitting. Numerical examples are presented to support the analytical results. Problems for future research are addressed.
Restoration of a Planar Image by Neural Network
For the restoration problem with disturbances of a planar black-and-white video-image we consider the restoring element with the aid of the formal neuron and propose the models of an optimal assignment of weights to its input dendrite channels. Also, the model of neuron adaptation is studied in detail using the stochastic approximation approach.
Design a model of Image Restoration using AI in Digital Image Processing
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Image restoration is the process of obtaining a distorted/noise image and giving an approximate clear image of the original image. False focus, motion blur and noise are forms of distortion. Image restoration can be done by reversing the process called Point Extension Function (PSF). In this process, the blurred image is generated by point source imaging and can be used to restore the image lost due to the blur process. Like to form. Modern artificial intelligence (AI) applied to image processing includes facial recognition, object recognition and detection, video, image action, and visual search. It helps to develop smart applications in digital image processing.
Implementation and Analysis of Image Restoration Techniques
International Journal of Computer Trends and …
IMAGE restoration is an important issue in high-level image processing. Images are often degraded during the data acquisition process. The degradation may involve blurring, information loss due to sampling, quantization effects, and various sources of noise. The purpose of image restoration is to estimate the original image from the degraded data. It is widely used in various fields of applications, such as medical imaging, astronomical imaging, remote sensing, microscopy imaging, photography deblurring, and forensic science, etc. Often the benefits of improving image quality to the maximum possible extent for outweigh the cost and complexity of the restoration algorithms involved. In this paper we are comparing various image restoration techniques like Richardson-Lucy algorithm, Wiener filter, Neural Network approach, on the basis of PSNR (Peak Signal to Noise Ratio).