Salvador Gabarda

Blind image quality assessment through anisotropy

We describe an innovative methodology for determining the quality of digital images. The method is based on measuring the variance of the expected entropy of a given image upon a set of predefined directions. Entropy can be calculated on a local basis by using a spatial/spatial-frequency distribution as an approximation for a probability density function. The generalized Rényi entropy and the normalized pseudo-Wigner distribution (PWD) have been selected for this purpose. As a consequence, a pixel-by-pixel entropy value can be calculated, and therefore entropy histograms can be generated as well. The variance of the expected entropy is measured as a function of the directionality, and it has been taken as an anisotropy indicator. For this purpose, directional selectivity can be attained by using an oriented 1-D PWD implementation. Our main purpose is to show how such an anisotropy measure can be used as a metric to assess both the fidelity and quality of images. Experimental results show that an index such as this presents some desirable features that resemble those from an ideal image quality function, constituting a suitable quality index for natural images. Namely, in-focus, noise-free natural images have shown a maximum of this metric in comparison with other degraded, blurred, or noisy versions. This result provides a way of identifying in-focus, noise-free images from other degraded versions, allowing an automatic and nonreference classification of images according to their relative quality. It is also shown that the new measure is well correlated with classical reference metrics such as the peak signal-to-noise ratio.

Cloud covering denoising through image fusion

This paper presents a solution to the cloud removal problem, based in a recently developed image fusion methodology consisting in applying a 1-D pseudo-Wigner distribution (PWD) transformation to the source images and on the use of a pixel-wise cloud model. Both features could also be interpreted as a denoising method centered in a pixel-level measure. Such procedure is able to process sequences of multi-temporal registered images affected with spatial-variant noise. The goal consists in providing a 2-D clean image, after removing the spatial-variant noise disturbing the set of multi-temporal registered source images. This is achieved by taking as reference a statistically parameterized model of a cloud prototype. Using this model, a pixel-wise measure of the noise degree of the source images can be calculated through their PWDs. This denoising procedure enables to choose the noise-free pixels from the set of given source images. The applicability of the method to the cloud removal paradigm is illustrated with different sets of artificial and natural cloudy or foggy images, partially occluded by clouds in different regions. Another advantage of the present approach is its reduced computational cost, once the 1-D case has been preferred instead of a full 2-D implementation of the PWD.

On the use of a joint spatial-frequency representation for the fusion of multi-focus images

This paper shows practical examples of the application of a new image fusion paradigm for achieving a 2-D all in-focus image starting from a set of multi-focus images of a 3-D real object. The goal consists in providing an enhanced 2-D image showing the object entirely in focus. The fusion procedure shown here is based on the use of a focusing pixel-level measure. Such measure is defined in the space-frequency domain through a 1-D pseudo-Wigner distribution. The method is illustrated with different sets of images. Evaluation measures applied to artificially blurred cut and pasted regions have shown that the present scheme can provide equally or even better performance than other alternative image fusion algorithms.

No-reference image quality assessment through the von Mises distribution.

An innovative way of calculating the von Mises distribution of image entropy is introduced in this paper. The von Mises distributions concentration parameter and some fitness parameter that will be defined later have been analyzed in the experimental part for determining their suitability as an image quality assessment measure in some particular distortions such as Gaussian blur or additive Gaussian noise. To achieve such measure, the local Rényi entropy is calculated in four equally spaced orientations and used to determine the parameters of the von Mises distribution of the image entropy. Considering contextual images, experimental results after applying this model show that the best-in-focus noise-free images are associated with the highest values for the von Mises distribution concentration parameter and the highest approximation of image data to the von Mises distribution model. Our defined von Mises fitness parameter experimentally appears also as a suitable no-reference image quality assessment indicator for no-contextual images.

Multifocus image fusion through pseudo-Wigner distribution

We describe a new image fusion paradigm that provides an enhanced image from a set of source images that present regions with different spatial degradation patterns. The fusion procedure is based on the use of a new defocusing pixel-level measure. Such a measure is defined through a 1-D pseudo-Wigner distribution function (PWD) applied to nonoverlapping N-pixel window slices of the original image. The process is repeated to cover the full image size. By taking a low-resolution image as a reference image, which can be defined by blurring and averaging the two source images, a pixel-level distance measure of the defocus degree can be obtained from the PWD of each image. This procedure makes it possible to choose from a focusing point of view the in-focus pixels from each one of the given source images. The method is illustrated with different pairs of images of the same scene, which are partly focused and partly defocused in different regions. The image fusion approach that we propose here can work for any source of images available, and the comparison using evaluation measures such as mean square error or percentage of correct decisions shows that our framework can outperform the current approaches for the analyzed cases. One additional advantage of the present approach is its reduced computational cost when compared with other methods based on a full 2-D implementation of the PWD.

Image analysis in modern ophthalmology: from acquisition to computer assisted diagnosis and telemedicine

Medical digital imaging has become a key element of modern health care procedures. It provides visual documentation and a permanent record for the patients, and most important the ability to extract information about many diseases. Modern ophthalmology thrives and develops on the advances in digital imaging and computing power. In this work we present an overview of recent image processing techniques proposed by the authors in the area of digital eye fundus photography. Our applications range from retinal image quality assessment to image restoration via blind deconvolution and visualization of structural changes in time between patient visits. All proposed within a framework for improving and assisting the medical practice and the forthcoming scenario of the information chain in telemedicine.

Image denoising and quality assessment through the Renyi entropy

This paper presents a new image denoising method based on truncating the original noisy coefficients of a Pseudo- Wigner distribution (PWD) calculated through 1D directional windows. This method has been tested both for additive and multiplicative noisy images. The coefficients are selected according to their local directionality to take into account the image anisotropy. Next, the PWD is inverted and the set of different directional images are averaged. When the ground truth image reference is available, the peak signal-to-noise ratio (PSNR) metric is used to evaluate the resulting denoised images in comparison with other alternative methods. The described method is based on the use of the Renyi entropy extracted from a joint spatial frequency representation such as the Wigner distribution. A comparison with other competitive techniques is described and tested for real-world images. In particular, some experimental results are presented in the area of synthetic aperture radar (SAR) and retinal imaging, showing the effectiveness of the method in comparison with other alternative techniques through the use of two different non-reference image quality metrics.

Anisotropy-based robust focus measure for non-mydriatic retinal imaging.

Non-mydriatic retinal imaging is an important tool for diagnosis and progression assessment of ophthalmic diseases. Because it does not require pharmacological dilation of the patients pupil, it is essential for screening programs performed by non-medical personnel. A typical camera is equipped with a manual focusing mechanism to compensate for the refractive errors in the eye. However, manual focusing is error prone, especially when performed by inexperienced photographers. In this work, we propose a new and robust focus measure based on a calculation of image anisotropy which, in turn, is evaluated from the directional variance of the normalized discrete cosine transform. Simulation and experimental results demonstrate the effectiveness of the proposed focus measure.

No-reference quality metrics for eye fundus imaging

This paper presents a comparative study on the use of noreference quality metrics for eye fundus imaging. We center on autofocusing and quality assessment as key applications for the correct operation of a fundus imaging system. Four state-of-the-art no-reference metrics were selected for the study. From these, a metric based of Renyi anisotropy yielded the best performance in both auto-focusing and quality assessment.

Discrimination of isotrigon textures using the Rényi entropy of Allan variances

We present a computational algorithm for isotrigon texture discrimination. The aim of this method consists in discriminating isotrigon textures against a binary random background. The extension of the method to the problem of multitexture discrimination is considered as well. The method relies on the fact that the information content of time or space-frequency representations of signals, including images, can be readily analyzed by means of generalized entropy measures. In such a scenario, the Rényi entropy appears as an effective tool, given that Rényi measures can be used to provide information about a local neighborhood within an image. Localization is essential for comparing images on a pixel-by-pixel basis. Discrimination is performed through a local Rényi entropy measurement applied on a spatially oriented 1-D pseudo-Wigner distribution (PWD) of the test image. The PWD is normalized so that it may be interpreted as a probability distribution. Prior to the calculation of the textures PWD, a preprocessing filtering step replaces the original texture with its localized spatially oriented Allan variances. The anisotropic structure of the textures, as revealed by the Allan variances, turns out to be crucial later to attain a high discrimination by the extraction of Rényi entropy measures. The method has been empirically evaluated with a family of isotrigon textures embedded in a binary random background. The extension to the case of multiple isotrigon mosaics has also been considered. Discrimination results are compared with other existing methods.