6533b835fe1ef96bd12a011b

RESEARCH PRODUCT

Non Linear Image Restoration in Spatial Domain

Laligant OlivierFauvet EricBushra Jalil

subject

Mathematical optimization[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingNoise reductionWiener filter020206 networking & telecommunications02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingNon-local meansMultiplicative noisesymbols.namesakeMean Square ErrorSignal-to-noise ratio[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingGaussian noiseSignal SmoothnessRestoration0202 electrical engineering electronic engineering information engineeringsymbols020201 artificial intelligence & image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingAlgorithmSmoothingImage restorationNonlinear FilteringMathematics

description

International audience; In the present work, a novel image restoration method from noisy data samples is presented. The restoration was per-formed by using some heuristic approach utilizing data samples and smoothness criteria in spatial domain. Unlike most existing techniques, this approach does not require prior modelling of either the image or noise statistics. The proposed method works in an interactive mode to find the best compromise between the data (mean square error) and the smoothing criteria. The method has been compared with the shrinkage approach, Wiener filter and Non Local Means algorithm as well. Experimental results showed that the proposed method gives better signal to noise ratio as compared to the previously proposed denoising solutions. Furthermore, in addition to the white Gaussian noise, the effectiveness of the proposed technique has also been proved in the presence of multiplicative noise.

https://hal.archives-ouvertes.fr/hal-00784954