A spatially-adaptive neural network approach to regularized image restoration

Alex S. Palmer, Moe Razaz, Danilo P. Mandic

Research output: Contribution to journalArticlepeer-review


When using a regularized approach for image restoration there is always a compromise between image sharpness and noise suppression. Since noise is removed at the cost of edges and detail within the image, there is a need to introduce algorithms which exhibit some kind of memory and cater for the spatial structure of an image. To this cause, we introduce an efficient restoration algorithm, based on a modified adaptive Hopfield neural network. The algorithm is capable of spatially regularizing an image and thereby preserving data fidelity around edges while simultaneously suppressing noise in more noticeable areas such as smooth regions. The proposed method demonstrates an improvement in restoration quality over existing adaptive and non-adaptive approaches. This is illustrated with simulations on benchmark images under varying noise levels.
Original languageEnglish
Pages (from-to)177-185
Number of pages9
JournalJournal of Intelligent and Fuzzy Systems
Issue number2-4
Publication statusPublished - Jan 2003

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