A block-oriented restoration in gray-scale images using full range autoregressive model

This paper introduces a novel approach, i.e. block oriented-restoration, based on a Family of Full Range Autoregressive (FRAR) model to restore the information lost, and this adopts the Bayesian approach to estimate the parameters of the model. The Bayesian approach, by combining the prior information and the observed data known as posterior distribution, makes inferences. The loss of information caused is due to errors in communication channels, through which the data are transmitted. In most applications, the data are transmitted block wise. Even if there is loss of a single bit in a block, it causes loss in the whole block and the impact may reflect on its consecutive blocks. In the proposed technique, such damaged blocks are identified, and to restore it, a priori information is searched and extracted from uncorrupted regions of the image; this information and the pixels in the neighboring region of the damaged block are utilized to estimate the parameters of the model. The estimated parameters are employed to restore the damaged block. The proposed algorithm takes advantage of linear dependency of the neighboring pixels of the damaged block and takes them as source to predict the pixels of the damaged block. The restoration is performed at two stages: first, the lone blocks are restored; second, the contiguous blocks are restored. It produces very good results and is comparable with other existing schemes.

► Proposed method is most appropriate for real life applications like Cable TV networks, since most data transmission techniques are block-based.
► Proposed restoration model has infinite order with finite number of (four) parameters only.
► Prior-information is searched throughout the whole image and it is combined with the neighboring pixel values of the information lost block.
► The combined information is used to estimate the parameters of the proposed restoration model.