There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.

/pdf/no-reference-image-quality-assessment-in-the-spatial-domain-2mnlvf25ig.pdf

No-Reference Image Quality Assessment in the Spatial Domain

An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip.

/pdf/making-a-completely-blind-image-quality-analyzer-4rsald8tn3.pdf

Making a “Completely Blind” Image Quality Analyzer

On robust estimation of the location parameter

The paper deals with the estimation of the maximal sparsity degree for which a given measurement matrix allows sparse reconstruction through l 1-minimization. This problem is a key issue in different applications featuring particular types of measurement matrices, as for instance in the framework of tomography with low number of views. In this framework, while the exact bound is NP hard to compute, most classical criteria guarantee lower bounds that are numerically too pessimistic. In order to achieve an accurate estimation, we propose an efficient greedy algorithm that provides an upper bound for this maximal sparsity. Based on polytope theory, the algorithm consists in finding sparse vectors that cannot be recovered by l 1-minimization. Moreover, in order to deal with noisy measurements, theoretical conditions leading to a more restrictive but reasonable bounds are investigated. Numerical results are presented for discrete versions of tomography measurement matrices, which are stacked Radon transforms corresponding to different tomograph views.

https://hal.archives-ouvertes.fr/hal-00878620/document

An Evaluation of the Sparsity Degree for Sparse Recovery with Deterministic Measurement Matrices

Natural texture images exhibit a high intra-class diversity due to different acquisition conditions (scene enlightenment, perspective angle, ... ). To handle with the diversity, a new supervised classification algorithm based on a parametric formalism is introduced: the K-centroids-based classifier (K-CB). A comparative study between various supervised classification algorithms on the VisTex and Brodatz image databases is conducted and reveals that the proposed K-CB classifier obtains relatively good classification accuracy with a low computational complexity.

/pdf/k-centroids-based-supervised-classification-of-texture-4ybw798ucb.pdf

K-centroids-based supervised classification of texture images: Handling the intra-class diversity

In this paper, we introduce a new generative adversarial network (GAN) with dual image-color discriminators, to predict Artificial-SAR colorized images from SAR ones (Sentinel-1). Based on the conventional architecture of GANs, we employ an additional color discriminator that evaluates the differences in brightness, contrast, and major colors between images, while the image discriminator compares texture and content. To achieve the required level of colorization in the generation process, we employ non-adversarial color loss dedicated for color comparison, unlike conventional approaches that use only L 1 loss. Moreover, to overcome the vanishing gradient problem in deep architecture, and ensure the flow of low-level information inside network layers, we add residual connections to our generator that follows the general shape of U-Net. The performance of the proposed model was evaluated quantitatively as well as qualitatively with the SEN1-2 dataset. Results show that the proposed model generates realistic colorized images with fewer artifacts compared to the state-of-the-art approaches. This model helps to maintain color steadiness as well as visual recognizability at less textured large continuous regions, such as plantation and water areas, when it's difficult to be distinguished in SAR images.

/pdf/dual-color-image-discriminators-adversarial-networks-for-464dixh03j.pdf

Dual Color-Image Discriminators Adversarial Networks for Generating Artificial-SAR Colorized Images from SENTINEL-1 Images

A new Riemannian geometry for the Compound Gaussian distribution is proposed. In particular, the Fisher information metric is obtained, along with corresponding geodesics and distance function. This new geometry is applied on a change detection problem on Multivariate Image Times Series: a recursive approach based on Riemannian optimization is developed. As shown on simulated data, it allows to reach optimal performance while being computationally more efficient.

Riemannian geometry for Compound Gaussian distributions: application to recursive change detection

Kernel based−methods using color histogram approach are recently used for visual tracking due to their efficiency and robustness. However, a color histogram suffers from the inability to encode spatial image information, thus images with very different appearances can have similar color histograms. This problem is especially critical in various applications such as retrieval for image and video databases. In this paper, we propose an alternative to color histogram called a multi−scale histogram for kernel based methods, which incorporates additional spatial information without sacrificing the robustness of these methods. We introduce a multi−scale histogram by taking histograms of several images scales and constructing a multidimensional histogram. We use this novel and simple feature for kernel−density estimates of the model and target distribution. Consequently, this approach leads to a very efficient nonparametric tracking algorithm.

Yannick Berthoumieu

Papers

An Evaluation of the Sparsity Degree for Sparse Recovery with Deterministic Measurement Matrices

K-centroids-based supervised classification of texture images: Handling the intra-class diversity

Dual Color-Image Discriminators Adversarial Networks for Generating Artificial-SAR Colorized Images from SENTINEL-1 Images

Riemannian geometry for Compound Gaussian distributions: application to recursive change detection

Multi-Scale Histograms For Kernel-Based Object Tracking