The restoration achieved on the basis of a Wiener scheme is an optimum since the restoration filter is the outcome of a minimisation process. Moreover, the Wiener restoration approach requires the estimation of some parameters related to the original image and the noise, as well as knowledge about the PSF function. However, in a real restoration problem, we may not possess accurate values of these parameters, making results relatively far from the desired optimum. Indeed, a desensitisation process is required to decrease this dependency on the parameter errors of the restoration filter. In this paper, we present an iterative method to reduce the sensitivity of a general restoration scheme (but specified to the Wiener filter) with regards to wrong estimates of the said parameters. Within the Fourier transform domain, a sensitivity analysis is tackled in depth with the purpose of defining a number of iterations for each frequency element, which leads to the aimed desensitisation regardless of the errors on estimates. Experimental computations using meaningful values of parameters are addressed. The proposed technique effectively achieves better results than those obtained when using the same wrong estimates in the Wiener approach, as well as verified on an SAR restoration.
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.
Ubimedia is a concept where media files are embedded in everyday objects and the environment. We propose an approach where the user can read and write these files with his/her personal mobile phone simply by touching the physical objects. This facilitates easy access and storage of, e.g. video and audio files related to the physical object in question. This paper describes our work in developing a technical solution for ubimedia and studying user acceptance of forthcoming ubimedia services. Our technical development of the ubimedia concept has been focused on a mobile phone platform with a tag reader/writer, memory tags with large storage capacity, and the communication between the phone and the tags. Currently, the technical design is in test and evaluation phase. The preliminary results show that the concept works and it can be implemented technically. In parallel with the technical development, we have studied usage possibilities for ubimedia and user acceptance of future ubimedia services. User acceptance has been studied in a web survey and in user evaluations of proofs-of-concept. In addition, an ethical assessment has been carried out. The users appreciated especially the simplicity, speed, low cost and reliability of ubimedia. Ethical concerns were related to control over the download with regard to viruses and other unwanted content.
Image restoration aims to restore an image within a given domain from a blurred and noisy acquisition. However, the convolution operator, which models the degradation, is truncated in a real observation causing significant artifacts in the restored results. Typically, some assumptions are made about the boundary conditions (BCs) outside the field of view to reduce the ringing. We propose instead a restoration method without prior conditions which reconstructs the boundary region as well as making the ringing artifact negligible. The algorithm of this article is based on a multilayer perceptron (MLP) which minimizes a truncated version of the total variation regularizer using a back-propagation strategy. Various experiments demonstrate the novelty of the MLP in the boundary restoration process without neither any image information nor prior assumption on the BCs.
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