Predicting blur visual discomfort for natural scenes by the loss of positional information.

The perception of blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is related to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment.

A Detail-Based Method for Linear Full Reference Image Quality Prediction.

In this paper, a novel full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details.

Two-dimensional approach to full-reference image quality assessment based on positional structural information.

A method for full-reference visual quality assessment based on the 2-D combination of two diverse metrics is described. The first metric is a measure of structural information loss based on the Fisher information about the position of the structures in the observed images. The second metric acts as a categorical indicator of the type of distortion that images underwent.

On the inter-conversion between Hermite and Laguerre local image expansions.

The nice relationship existing among the Hermite-Gauss and the Laguerre-Gauss image expansions, whose basis functions span the same signal space, is investigated. As a result, a novel efficient method for Cartesian to polar coordinate inter-conversion, especially suited for dedicated hardware realization, is proposed. Applications to local image rotation, based on the simple steerability of the Laguerre-Gauss expansion, and to local image analysis, based on Gaussian derivatives, are considered in detail.

Maximum likelihood orientation estimation of 1-D patterns in Laguerre-Gauss subspaces.

A method for measuring the orientation of linear (1-D) patterns, based on a local expansion with Laguerre-Gauss circular harmonic (LG-CH) functions, is presented. It lies on the property that the polar separable LG-CH functions span the same space as the 2-D Cartesian separable Hermite-Gauss (2-D HG) functions. Exploiting the simple steerability of the LG-CH functions and the peculiar block-linear relationship among the two expansion coefficients sets, maximum likelihood (ML) estimates of orientation and cross section parameters of 1-D patterns are obtained projecting them in a proper subspace of the 2-D HG family.

Maximum likelihood localization of 2-D patterns in the Gauss-Laguerre Transform domain: theoretic framework and preliminary results.

Usual approaches to localization, i.e., joint estimation of position, orientation and scale of a bidimensional pattern employ suboptimum techniques based on invariant signatures, which allow for position estimation independent of scale and orientation.