Journal of Beijing University of Aeronautics and A stronautics PCNN, PCNN. Nove l adap tive deno ising m e thod fo r extrem e no ise ba sed on PCNN

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1 Journal of Beijing University of Aeronautics and A stronautics January 2009 Vol. 35 No11 PCNN (, ) : PCNN ( Pulse Coup led Neural Network) ADEN (Adapative Denosing method for Extreme Noise) 2PCNN., ;, PCNN PCNN,,.. : ; ; ( PCNN) ; : TP : A : (2009) Nove l adap tive deno ising m e thod fo r extrem e no ise ba sed on PCNN L iu Yuanm in Q in Shiyin ( School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and A stronautics, Beijing , China) Ab strac t: To imp lement the removal of salt and pepper noise effectively and the conservation of image details and textures, a novel adap tive denoising method for extrem e noise based2on pulse coup led neural net2 work (ADEN2PCNN) was p roposed. A kind of detection mechanism was app lied to discrim inate whether a giv2 en p ixel was corrup ted or not, and only the corrup ted p ixels must be denoised so that the original image infor2 mation could not be damaged and the details as well as the textures of the images clould be conserved effective2 ly. To imp rove the image quality, the self2organization m echanism was introduced into PCNN array framework, thus the neighboring connection modes of neurons in the PCNN could sw itch automatically. Furthermore, an a2 dap tive mechanism was used to automatically select the op timal filtering times based on the estimated noise in2 tensity to enhance adap tability of algortihm. Experiment results indicate that this m ethod p resented is more p reponderant than the conventional m ethods and other congeneric methods in removing noise and conserving image details. Ke y wo rd s: image denoising; noise detection; pulse coup led neural network ( PCNN) ; adap tive filtering,,.,,,,,. PCNN ( Pulse Coup led Neural Network) [ 1-2 ], 3, [ 3 ], [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8-9 ]. [ 8 ] PCNN,, : : ( ),,,, liu_yuan_m in@163. com.

2 1 : PCNN 3 3,,. [ 9 ],,,,,. [ 8-9 ],,,,,,. 1 PCNN PCNN 3 :,,, 1. 4 : ij ( k) F ij ( k) = e - F t F ij ( k - 1) + S ij + U ij ( k) Y ij ( k) V F M ijk l Y k l ( k - 1) (1) = F ij ( k) (1 + L ij ( k) ) (2) = e - t ij ( k - 1) + V Y ij ( k - 1) (3) = step (U ij ( k) - ij ( k) ) = 1 U ij > ij 0 (4), ij ( i, j) ; F ij ; S ij ; M ijk l ( k, l) ( i, j) ; L ij, ; U ij ; ij ( dynam ic threshold) ; Y ij ; k k ; ; t. V F, V ; F,., ij (0) = uv ( 0),, x ij, x uv ( ij ( 0) > x ij > x uv )., k = 0, ( 3 ),, ij ( k) = ij (0) exp ( - k t) < x ij, ( i, j) 1,. ( u, v) L uv ( k + 1) 0, ( k + 1 ) ( u, v) U uv = x uv (1 + L uv ( k + 1) ). x uv U uv, U uv = x uv ( 1 + L uv ( k + 1) ) > uv, ( u, v). ( u, v) ( i, j),.,., [ 10 ],., PCNN,,. 2 PCNN 2. 1 PCNN PCNN,,,, :. 2 N ij 3 3, F,, W, (5), L ij (6). 7 7, 2. W pq = ( p, q) V pq ( p, q) (5) 1 PCNN PCNN,,.,. ( i, j), ( u, v) 2 3 3

3 i+1 j+1 L ij = p = i- 1 q = j- 1 W pq Y pq - W ij Y ij (6), V pq ( p, q) ; Y pq ( p, q), Y pq = 1, 0.,.,,.,,, ;, ;, [ 8 ]..,,,., PCNN. 8, 0 255,, , ( i, j) 7 7 W 7 7, ( i, j) 0 255, 0 255,,,,, ,.,,, PCNN. PCNN : 0. 5, 3 3 ; ( 7), 6, : l = 1 0 < < < < < > PCNN (7), ADEN (Adapative Denosing method for Extreme Noise) 2PCNN, PCNN,,,. ADEN2PCNN. 1, m + 6, n + 6, m, n. 2 sum = 0; ( i, j), x ( i, j) = 255 0, x ( p, q) (0, 255), ( p, q) W 7 7, x ( i, j), sum + + ; = sum / (m n) ; ( 7) l, PCNN. 3 PCNN = , N = 5, = 0. 01, V = 0, t = 1,M ijk l = 0, PCNN,. 4 for t = 1 to 2 If t = 1 then = 255 If t = 2 then = 1 for p = 1 ton 1) (2) (5) U ij. 2) (3). 3) (4),. 4) :,,. 5) :. End for End for t.

4 1 : PCNN 111 5, 4,. 3, , 0. 4, 0. 5, 0. 6, 0. 7, PSNR ( Peak Signal to Noise Ratio) 9. 12, 8. 19, 7. 40, 6. 7 db, 4. 3 ADEN2PCNN a ,,,., PCNN, 0. 6, PCNN ,. 4,,,. 1, PSNR,.,. [ 9 ] : PCNN,, PCNN,. PSNR /db 1 PSNR /db ADEN2PCNN b 0. 5 c 0. 6 d PCNN ADEN2PC2 NN,,. PCNN,,,,., PCNN,,. ( References) [ 1 ] Eckhorn R, Reitboeck H J, A rndt M, et al. Feature linking via synchronization among distributed assemblies: simulation of re2

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