Joint binary image deconvolution and blur identification in the context of two-dimensional storage channels

We consider blurring of binary images and corruption by ambient noise occuring on two-dimensional storage channels. Since coding is generally used in such systems, the deconvolution problem can be treated jointly with decoding. Several methods have been proposed in the literature under the name of turbo equalization to mitigate the degradation introduced by such channels. However, the problem of blur identification has rarely been addressed previously. In this paper, we propose a technique for estimating the 2D channel coefficients, along with the variance of the ambient noise. The proposed estimation algorithm is adaptive and performed jointly with turbo equalization, so as to limit the number of known pilot symbols needed to bootstrap the channel estimator. Interestingly, we found that the computational complexity of the proposed joint channel estimation and turbo equalization method depends heavily on the sensitivity of existing turbo equalization methods to 2D channel parameter mismatch.

► Parameter identification of a 2D storage channel can be performed jointly with equalization and decoding.
► Only a small number of known pilot symbols in a stored 2D binary image is required.
► The computational complexity of joint 2D channel estimation, equalization and decoding depends heavily on the sensitivity of the adopted equalization algorithm to 2D channel parameter mismatch.