Image denoising using grey relational analysis in spatial domain

The search for efficient image denoising methods still is an open question. In this paper, we develop a universal algorithm based on grey relational analysis for similar binary image denoising. After taking the differences in spatial distribution between noise and edge into consideration, we select two referential sequences to represent inner off/on pixels, and a group of comparative sequences to stand for the pixels to be processed. Then, by analyzing the grey relational coefficients of the two kinds of sequences, we distinguish edge pixels from non-edge pixels, and reset the non-edge pixels to binary, thereby noise reduced and edges kept. The interest of the method lies in the fact that, without any precedent knowledge, it not only can reduce speckle, salt & pepper and gaussian noise at one time but also provide a trade-off between edge reservation and noise reduction via grey relational threshold. Experimental results show that the method obviously outperforms the three conventional spatial filters: median filter, wiener filter and mean filter. Possible applications include the processing before the recognition of printed or handwritten character, vehicle license plate, and the optimization of scanned binary trademark, engineering drawing and extracted binary watermark.