Multiscale structure based image representation using a set of regions
The application fields are (i) of appropriate granularity for best image compression, (ii) of appropriately rescaled size for image magnification or superresolution, and (iii) for smoothing for image quality restoration through structure-preserving denoising.
Structure Based Image Denoising
This work addresses the problem of denoising of images corrupted by AWGN. The Wiener filter is optimum in minimizing the mean-square-error under suitable assumptions of stationarity of the signal statistics. Locally, such assumptions are reasonable, as in the adaptive realization of theWiener filter whose performance is among the best known till date. Over the last few years, there has been much interest in threshold based denoising schemes. In this paper we present a novel framework for denoising signals from their compact representation in multiple domains. Each domain captures, uniquely, certain signal characteristics better than others. We define confidence sets around data in each domain and find sparse estimates that lie in the intersection of these sets, using a POCS algorithm. Simulations demonstrate the superior nature of the reconstruction (both in terms of mean-square error and perceptual quality) in comparison to the adaptive Wiener filter.
The following images compare the performance of our scheme to that of Donoho and Johnstone’s.
- K. Ratakonda and N. Ahuja, Restoring Image Quality Through Structure Preserving De-Noising, 3rd Asian Conference on Computer Vision, Hong Kong, January 8-11, 1998, 33-40.
- P. Ishwar, K. Ratakonda, P. Moulin and N. Ahuja, Image De-noising Using Multiple Compaction Domains, International Conference on Acoustics, Speech, and Signal Processing, Vol. III, Seattle, WA, May 12-15, 1998, Invited, 1889-1892.
- M. Singh, P. Ishwar, K. Ratakonda and N. Ahuja, Segmentation Based Denoising Using Multiple Compaction Domains, International Conference on Image Processing, Kobe, Japan, Oct. 1999, I-372-375.