Handling noise in image deconvolution with local/non-local priors

Hicham Badri (1) , Hussein Yahia (1)

1 GeoStat - Geometry and Statistics in acquisition data

Non-blind deconvolution consists in recovering a sharp latent image from a blurred image with a known kernel. Decon-volved images usually contain unpleasant artifacts due to the ill-posedness of the problem even when the kernel is known. Making use of natural sparse priors has shown to reduce ring-ing artifacts but handling noise remains limited. On the other hand, non-local priors have shown to give the best results in image denoising. We propose in this paper to combine both local and non-local priors to handle noise. We show that the blur increases the self-similarity within an image and thus makes non-local priors a good choice for denoising blurred images. However, denoising introduces outliers which are not Gaussian and should be well modeled. Experiments show that our method produces a better image reconstruction both visually and empirically compared to methods some popular methods.

Mots clés: Image deconvolution; deblurring; non-local prior; self-similarity; sparsity

Domaines: Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Hicham Badri, Hussein Yahia. Handling noise in image deconvolution with local/non-local priors. IEEE International Conference on Image Processing (ICIP), IEEE, Oct 2014, Paris, France. ⟨hal-01078693⟩