Laser tunable Toraldo superresolution with a uniform nonlinear pupil filter

We demonstrate that it is possible to reduce the focal spot size by inserting a uniform nonlinear thin film at the aperture of a focusing lens. The reduction of spot size is tunable by adjusting the incoming laser power. In comparison with the original diffraction spot, the transverse spot size can be reduced 0.65 times. The nonlinear thin film acts effectively as a Toraldo filter, and the phase and amplitude modulation stems from the laser-induced variances in the transmission of the thin film. The proposed technique removes the need of fabricating annular pupil filters. (C) 2008 Optical Society of America.

Comparison of uniform amplitude beam and intercepted Gaussian beam superresolution theories

The general superresolution theories for uniform amplitude beams and intercepted Gaussian beams are investigated. For these two types of incident beam, both two-zone amplitude and pure-phase filters are adopted to provide specific numerical descriptions of their differences in superresolution performances. Simulated results of comparisons between their performances indicate that, with the same spot size ratio, the intercepted Gaussian beam achieves a higher central image brightness ratio and significantly lower side-lobe effect irrespective of the filter used. (c) 2008 Elsevier Ltd. All rights reserved.

Superresolution: an ill-posed problem in Fourier optics

info:eu-repo/semantics/published

The Superresolution Problem in Doppler Difference Laser Anemometry

info:eu-repo/semantics/published

Superresolution and number of degrees of freedom in inverse diffraction with subwavelength sources

info:eu-repo/semantics/published

Superresolution in linear inverse problems

info:eu-repo/semantics/published

Anamorphic pixels for multi-channel superresolution

Superresolution from plenoptic cameras or camera arrays is usually treated similarly to superresolution from video streams. However, the transformation between the low-resolution views can be determined precisely from camera geometry and parallax. Furthermore, as each low-resolution image originates from a unique physical camera, its sampling properties can also be unique. We exploit this option with a custom design of either the optics or the sensor pixels. This design makes sure that the sampling matrix of the complete system is always well-formed, enabling robust and high-resolution image reconstruction. We show that simply changing the pixel aspect ratio from square to anamorphic is sufficient to achieve that goal, as long as each camera has a unique aspect ratio. We support this claim with theoretical analysis and image reconstruction of real images. We derive the optimal aspect ratios for sets of 2 or 4 cameras. Finally, we verify our solution with a camera system using an anamorphic lens.

Feature-domain super-resolution for IRIS recognition

Uncooperative iris identification systems at a distance suffer from poor resolution of the captured iris images, which significantly degrades iris recognition performance. Superresolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, all existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values. This paper considers transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. This is the first paper to investigate the possibility of feature domain super-resolution for iris recognition, and experiments confirm the validity of the proposed approach.

Feature-domain super-resolution framework for gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics.