@article {270,
	title = {Fast phase-based registration of multimodal image data},
	journal = {Signal Processing},
	volume = {89},
	year = {2010},
	pages = {724 - 737},
	abstract = {<p>An interesting problem in pattern recognition is that of image registration, which plays an important role in many vision-based recognition and motion analysis applications. Of particular interest among registration problems are multimodal registration problems, where the images exist in different feature spaces. State-of-the-art phased-based approaches to multimodal image registration methods have provided good accuracy but have high computational cost. This paper presents a fast phase-based approach to registering multimodal images for the purpose of initial coarse-grained registration. This is accomplished by simultaneously performing both globally exhaustive dynamic phase sub-cloud matching and polynomial feature space transformation estimation in the frequency domain using the fast Fourier transform (FFT). A multiscale phase-based feature extraction method is proposed that determines both the location and size of the dynamic sub-clouds being extracted. A simple outlier pruning based on resampling is used to remove false keypoint matches. The proposed phase-based approach to registration can be performed very efficiently without the need for initial estimates or equivalent keypoints from both images. Experimental results show that the proposed method can provide accuracies comparable to the state-of-the-art phase-based image registration methods for the purpose of initial coarse-grained registration while being much faster to compute.</p>
},
	keywords = {Dynamic sub-clouds, Fast Fourier Transform, image registration, Keypoints, Multimodal, phase},
	issn = {0165-1684},
	doi = {http://dx.doi.org/10.1016/j.sigpro.2008.10.028},
	author = {A Wong and P Fieguth}
}
@article {226,
	title = {Phase-adaptive superresolution of mammographic images using complex wavelets},
	journal = {IEEE Transactions on Image Processing},
	volume = {18},
	year = {2010},
	pages = {1140 - 1146},
	abstract = {<p>This correspondence describes a new superresolution approach for enhancing the resolution of mammographic images using complex wavelet frequency information. This method allows regions of interest of a mammographic image to be viewed in enhanced resolution while reducing the patient exposure to radiation. The proposed method exploits the structural characteristics of breast tissues being imaged and produces higher resolution mammographic images with sufficient visual fidelity that fine image details can be discriminated more easily. In our approach, the superresolution problem is formulated as a constrained optimization problem using a third-order Markov prior model and adapts the priors based on the phase variations of the low-resolution mammographic images. Experimental results indicate the proposed method is more effective at preserving the visual information when compared with existing resolution enhancement methods.</p>
},
	keywords = {adaptive, mammography, phase, superresolution},
	author = {A Wong and J Scharcanski}
}
