TY - EJOUR
DO - 10.1109/TIP.2007.906002
AB - We propose a new approach to image denoising, based on the image-domain minimization of an estimate of the mean squared error—Stein's unbiased risk estimate (SURE). Unlike most existing denoising algorithms, using the SURE makes it needless to hypothesize a statistical model for the noiseless image. A key point of our approach is that, although the (nonlinear) processing is performed in a transformed domain—typically, an undecimated discrete wavelet transform, but we also address nonorthonormal transforms—this minimization is performed in the image domain. Indeed, we demonstrate that, when the transform is a “tight” frame (an undecimated wavelet transform using orthonormal filters), separate subband minimization yields substantially worse results. In order for our approach to be viable, we add another principle, that the denoising process can be expressed as a linear combination of elementary denoising processes—linear expansion of thresholds (LET). Armed with the SURE and LET principles, we show that a denoising algorithm merely amounts to solving a linear system of equations which is obviously fast and efficient. Quite remarkably, the very competitive results obtained by performing a simple threshold (image-domain SURE optimized) on the undecimated Haar wavelet coefficients show that the SURE-LET principle has a huge potential.
T1 - The SURE-LET Approach to Image Denoising
IS - 11
DA - 2007
AU - Blu, T.
AU - Luisier, F.
JF - IEEE Transactions on Image Processing
SP - 2778–2786
VL - 16
EP - 2778–2786
PB - Institute of Electrical and Electronics Engineers
ID - 130342
KW - SURE-LET Denoising
KW - CIBM-SP
SN - 1057-7149
UR - http://bigwww.epfl.ch/publications/blu0702.ps
UR - http://bigwww.epfl.ch/publications/blu0702.html
UR - http://infoscience.epfl.ch/record/130342/files/blu0702.pdf
ER -