On sparse representations of color images

We investigate an intrinsic and useful form of sparsity of color images that was largely overlooked in the literature of image/video processing. This sparsity of multispectral images is revealed and formulated by modeling the image formation process. The underlying new sparse representations of color images are general and can be exploited to improve the performance of existing image restoration algorithms, such as denoising, deblurring, and resolution upconversion.

[1]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[2]  Alin Achim,et al.  18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011 , 2011, ICIP.

[3]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[4]  Ronen Basri,et al.  Lambertian reflectance and linear subspaces , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Bui Tuong Phong Illumination for computer generated pictures , 1975, Commun. ACM.

[6]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.