Wavelet-Based Blind Superresolution from Video Sequence and in MRI
N.K. Bose · Defense Technical Information Center (DTIC) · 2005
The first contribution of this research is the development of a mathematical framework for deployment of second-generation wavelets for image superresolution. Second, the Biggs-Andrews multichannel iterative blind deconvolution (IBD) algorithm is modified to include the blur support estimation module. Then the asymmetry factor for the Richardson-Lucy update-based IBD algorithm is calculated. Simulations conducted on real-world and synthetic images confirm the importance of accurate support estimation in the blind superresolution problem. The effect of the threshold level on reconstructed image quality in second-generation wavelet superresolution is investigated and a measure based on the singular values of the image matrix is employed as a reliable gauge of visual image quality. A discrete implementation of the moving least squares (MLS) is made on images and the effect of choice of the two dependent parameters, scale and order, on noise filtering and reduction of blur introduced during the MLS process is studied. Finally, the role of brushlets in textured image denoising and segmentation is investigated and plans made for future research in miniaturized computational imaging systems for superresolution with increased field of view.