CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.04 - April
Issue No.04 - April (2013 vol.35)
A. Rajwade , DA-IICT, Gandhinagar, India
A. Rangarajan , Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA
A. Banerjee , Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA
In this paper, we propose a very simple and elegant patch-based, machine learning technique for image denoising using the higher order singular value decomposition (HOSVD). The technique simply groups together similar patches from a noisy image (with similarity defined by a statistically motivated criterion) into a 3D stack, computes the HOSVD coefficients of this stack, manipulates these coefficients by hard thresholding, and inverts the HOSVD transform to produce the final filtered image. Our technique chooses all required parameters in a principled way, relating them to the noise model. We also discuss our motivation for adopting the HOSVD as an appropriate transform for image denoising. We experimentally demonstrate the excellent performance of the technique on grayscale as well as color images. On color images, our method produces state-of-the-art results, outperforming other color image denoising algorithms at moderately high noise levels. A criterion for optimal patch-size selection and noise variance estimation from the residual images (after denoising) is also presented.
Noise measurement, Transforms, Noise reduction, Image denoising, PSNR, Singular value decomposition, patch similarity, Image denoising, singular value decomposition (SVD), higher order singular value decomposition (HOSVD), coefficient thresholding, learning orthonormal bases
A. Rajwade, A. Rangarajan, A. Banerjee, "Image Denoising Using the Higher Order Singular Value Decomposition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 4, pp. 849-862, April 2013, doi:10.1109/TPAMI.2012.140