This article presents a local entropy based image reconstruction algorithm that performs quite well in cases where there is distortion in an image. If the wanted image information is still available but distributed over two or more distinct images, the algorithm can collect the required information from the set of the images given. Instead of inferring pixel data information from the remainder of one single image, the algorithm provides a decision rule on what information from which one of the set of given images to actually use in order to create a new, (ideally) distortionfree image. --
The definition of Shannon's entropy in the context of information theory is critically examined and ...
Abstract: The definition of Shannon's entropy in the context of infonnation theory is criticall...
International audienceIn this paper we address the image restoration problem in the variational fram...
Presented are two new methods based on entropy for reconstructing images compressed with the Discret...
A new, universal approach to reconstructing transform-coded images is proposed. The method views the...
Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent rese...
Recently, learned image compression methods have outperformed traditional hand-crafted ones includin...
International audienceIn this paper we address the image restoration problem in the variational fram...
Image analysis is playing a very essential role in numerous research areas in the fields of science ...
Shannon's definition of entropy is critically examined and a new definition of classical entropy bas...
Many imaging systems are faced with the problem of estimating a true image from a degraded dataset. ...
Abstract- Shannon’s definition of entropy is critically examined and a new definition of classical e...
Entropy, the key factor of information theory, is one of the most important research areas in comput...
The maximum entropy method (MEM) of image reconstructtion is discussed in the context of incomplete ...
An alternative motivation for the maximum entropy method (MEM) is given and its practical implementa...
The definition of Shannon's entropy in the context of information theory is critically examined and ...
Abstract: The definition of Shannon's entropy in the context of infonnation theory is criticall...
International audienceIn this paper we address the image restoration problem in the variational fram...
Presented are two new methods based on entropy for reconstructing images compressed with the Discret...
A new, universal approach to reconstructing transform-coded images is proposed. The method views the...
Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent rese...
Recently, learned image compression methods have outperformed traditional hand-crafted ones includin...
International audienceIn this paper we address the image restoration problem in the variational fram...
Image analysis is playing a very essential role in numerous research areas in the fields of science ...
Shannon's definition of entropy is critically examined and a new definition of classical entropy bas...
Many imaging systems are faced with the problem of estimating a true image from a degraded dataset. ...
Abstract- Shannon’s definition of entropy is critically examined and a new definition of classical e...
Entropy, the key factor of information theory, is one of the most important research areas in comput...
The maximum entropy method (MEM) of image reconstructtion is discussed in the context of incomplete ...
An alternative motivation for the maximum entropy method (MEM) is given and its practical implementa...
The definition of Shannon's entropy in the context of information theory is critically examined and ...
Abstract: The definition of Shannon's entropy in the context of infonnation theory is criticall...
International audienceIn this paper we address the image restoration problem in the variational fram...