Handling noise in image deconvolution with local/non-local priors - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

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

Hicham Badri
  • Function : Author
  • PersonId : 772557
  • IdRef : 19070506X
Hussein Yahia


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.
Fichier principal
Vignette du fichier
Handling Noise in Image Deconvolution with Local&Non-Local Priors_icip2014.pdf (2.35 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01078693 , version 1 (29-10-2014)


  • HAL Id : hal-01078693 , version 1


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⟩


188 View
267 Download


Gmail Facebook X LinkedIn More