@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 = {

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.

}, keywords = {adaptive, mammography, phase, superresolution}, author = {A Wong and J Scharcanski} } @article {315, title = {Adaptive bilateral filtering of image signals using local phase characteristics}, journal = {Signal Processing}, volume = {88}, year = {2008}, pages = {1615 - 1619}, abstract = {

This paper presents a novel perceptually based method for noise reduction of image signals characterized by low signal to noise ratios. The proposed method exploits the local phase characteristics of an image signal to perform bilateral filtering in an adaptive manner. The proposed method takes advantage of the human perception system to preserve perceptually significant signal detail while suppressing perceptually significant noise in the image signal. Experimental results show that the proposed method is effective at removing signal noise while enhancing perceptual quality both quantitatively and qualitatively.

}, keywords = {adaptive, Bilateral filtering, denoising, Local phase}, author = {A Wong} } @inproceedings {372, title = {Adaptive multiple texture approach to texture packing for 3D video games}, booktitle = {2007 Conference on Future Play}, year = {2007}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

This paper presents an adaptive multiple texture approach to the problem of texture packing for 3D video games. In modern graphics hardware, texture size is typically constrained to width and height dimensions that are powers of two. To reduce the texture management overhead caused by storing individual textures, texture packing algorithms are used to pack multiple textures into a single powers-of-two texture. Current texture packing techniques are very limiting as they are capable of packing textures only into a single texture of predefined size. This can result in significant wasted texture space due to the powers-of-two texture size restrictions. In the proposed technique, individual arbitrarily sized rectangular textures are packed into multiple textures in an adaptive manner. This approach reduces the amount of wasted texture space in a more efficient manner by adaptively determining the quantity as well as size of textures being used during the packing process. Experimental results demonstrate the effectiveness of this technique in packing textures in an efficient and automated fashion. This makes it well suited for improving texture management in future 3D video games, where resources are limited and a high frame rate needs to be achieved to provide a truly immersive experience.

}, keywords = {3D, adaptive, texture packing, video games}, isbn = {978-1-59593-943-2}, doi = {http://doi.acm.org/10.1145/1328202.1328236}, author = {A Wong and A Kennings} }