[IPOL announce] new article: Data Adaptive Dual Domain Denoising: a Method to Boost State of the Art Denoising Algorithms

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2017/203/

Nicola Pierazzo, and Gabriele Facciolo,
Data Adaptive Dual Domain Denoising: a Method to Boost State of the Art 
Denoising Algorithms,
Image Processing On Line, 7 (2017), pp. 93–114.
https://doi.org/10.5201/ipol.2017.203

Abstract
This article presents DA3D (Data Adaptive Dual Domain Denoising), a 
'last step denoising' method that takes as input a noisy image and as a 
guide the result of any state-of-the-art denoising algorithm. The method 
performs frequency domain shrinkage on shape and data-adaptive patches. 
DA3D doesn't process all the image samples, which allows it to use large 
patches (64 x 64 pixels). The shape and data-adaptive patches are 
dynamically selected, effectively concentrating the computations on 
areas with more details, thus accelerating the process considerably. 
DA3D also reduces the staircasing artifacts sometimes present in smooth 
parts of the guide images. The effectiveness of DA3D is confirmed by 
extensive experimentation. DA3D improves the result of almost all 
state-of-the-art methods, and this improvement requires little 
additional computation time.