2,255 research outputs found
Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
As a powerful statistical image modeling technique, sparse representation has
been successfully used in various image restoration applications. The success
of sparse representation owes to the development of l1-norm optimization
techniques, and the fact that natural images are intrinsically sparse in some
domain. The image restoration quality largely depends on whether the employed
sparse domain can represent well the underlying image. Considering that the
contents can vary significantly across different images or different patches in
a single image, we propose to learn various sets of bases from a pre-collected
dataset of example image patches, and then for a given patch to be processed,
one set of bases are adaptively selected to characterize the local sparse
domain. We further introduce two adaptive regularization terms into the sparse
representation framework. First, a set of autoregressive (AR) models are
learned from the dataset of example image patches. The best fitted AR models to
a given patch are adaptively selected to regularize the image local structures.
Second, the image non-local self-similarity is introduced as another
regularization term. In addition, the sparsity regularization parameter is
adaptively estimated for better image restoration performance. Extensive
experiments on image deblurring and super-resolution validate that by using
adaptive sparse domain selection and adaptive regularization, the proposed
method achieves much better results than many state-of-the-art algorithms in
terms of both PSNR and visual perception.Comment: 35 pages. This paper is under review in IEEE TI
A novel multispectral and 2.5D/3D image fusion camera system for enhanced face recognition
The fusion of images from the visible and long-wave infrared (thermal) portions of the spectrum
produces images that have improved face recognition performance under varying lighting conditions.
This is because long-wave infrared images are the result of emitted, rather than reflected,
light and are therefore less sensitive to changes in ambient light. Similarly, 3D and 2.5D images
have also improved face recognition under varying pose and lighting. The opacity of glass to
long-wave infrared light, however, means that the presence of eyeglasses in a face image reduces
the recognition performance.
This thesis presents the design and performance evaluation of a novel camera system which is
capable of capturing spatially registered visible, near-infrared, long-wave infrared and 2.5D depth
video images via a common optical path requiring no spatial registration between sensors beyond
scaling for differences in sensor sizes. Experiments using a range of established face recognition
methods and multi-class SVM classifiers show that the fused output from our camera system not
only outperforms the single modality images for face recognition, but that the adaptive fusion
methods used produce consistent increases in recognition accuracy under varying pose, lighting
and with the presence of eyeglasses
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