Deep learning (DL) networks have recently achieved excellent performance on image compressed sensing. However, most existing methods rely on burdened and complex network structures, resulting in significant computational and storage requirements that defeat the purpose of compressed sensing. This severely hinders their applicability in real-world resource-limited devices. In this paper, a lightweight cycle network driven by physical and deep priors for image compressed sensing is proposed which integrates the learning of the sensing matrix and compressive image reconstruction. Specifically, the regularization terms and a likelihood term derived from the physical observation model are learned in an end-to-end cycle network, simultaneously estimating the reconstructed image and sensing matrix in the image and feature domains. Moreover, a dual-domain fusion reconstruction module is proposed. It creates simulated measurement residuals for enhancing reconstruction in the compressed domain, which leads to high reconstruction performance and reduces computational load by bonding together the compressed image domains in the cyclic network. Extensive experiments demonstrate that our model delivers superior performance and alleviates model complexity, which is of great importance in low-budget applications.

In this paper we propose the use of the Hyperbolic Secant (HS) distribution as a prior for the Blind Image Deconvolution (BID) problem. It is well-known that when high-pass filters are applied to natural images, the resulting coefficients are sparse. We leverage this property using the HS distribution, a seldom explored Super Gaussian distribution with suitable properties for this problem. Using the Pólya-Gamma distribution, we derive an explicit Gaussian Scale Mixture representation. This representation is then used to propose a novel variational Bayesian algorithm that outperforms state-of-the-art BID methods.

Hematoxylin and Eosin (H&E) color variation among histological images from different laboratories can significantly degrade the performance of Computer-Aided Diagnosis systems. The staining procedure is the primary factor responsible for color variation, and consequently, the methods designed to reduce such variations are designed in concordance with this procedure. In particular, Blind Color Deconvolution (BCD) methods aim to identify the true underlying colors in the image and to separate the tissue structure from the color information. Unfortunately, BCD methods often assume that images are stained solely with pure staining colors (e.g., blue and pink for H&E). This assumption does not hold true when common artifacts such as blood are present, requiring an additional color component to represent them. This is a challenge for color standardization algorithms, which are unable to correctly identify the stains in the image, leading to unexpected results. In this work, we propose a Blood-Robust Bayesian K-Singular Value Decomposition model designed to simultaneously detect blood and extract color from histological images while preserving structural details. We evaluate our method using both synthetic and real images, which contain varying amounts of blood pixels. • Blind Color Deconvolution separates color from structure in histological images. • Few BCD methods consider the effects of noise and artifacts. • We propose a blood robust approach that combines blood detection with BCD. • The proposed approach significantly improves the quality of stain separation in the presence of blood.

Histological images are often tainted with two or more stains to reveal their underlying structures and conditions. Blind Color Deconvolution (BCD) techniques separate colors (stains) and structural information (concentrations), which is useful for the processing, data augmentation, and classification of such images. Classical BCD methods rely on a complicated optimization procedure that has to be carried out on each image independently, i.e., they are not amortized methods. In contrast, once they have been trained, deep neural networks can be used in a fast, amortized manner on unseen inputs. Unfortunately, the lack of large databases of ground truth color and concentrations has limited the development of deep models for BCD. In this work, we propose a deep variational Bayesian BCD neural network (BCD-Net) for stain separation and concentration estimation. BCD-Net is trained by maximizing the evidence lower bound of the observed images, which does not require the use of ground truth examples of stains and concentrations. Results obtained using two multicenter databases (Camelyon-17 and a stain separation benchmark) demonstrate the effectiveness of BCD-Net in the stain separation tasks, while drastically reducing the computation time compared to classical non-amortized methods.

Network anomaly detection is a very relevant research area nowadays, especially due to its multiple applications in the field of network security. The boost of new models based on variational autoencoders and generative adversarial networks has motivated a reevaluation of traditional techniques for anomaly detection. It is, however, essential to be able to understand these new models from the perspective of the experience attained from years of evaluating network security data for anomaly detection. In this paper, we revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view, and contribute a mathematical model that relates them. Specifically, we start with the probabilistic PCA model and explain its connection to the Multivariate Statistical Network Monitoring (MSNM) framework. MSNM was recently successfully proposed as a means of incorporating industrial process anomaly detection experience into the field of networking. We have evaluated the mathematical model using two different datasets. The first, a synthetic dataset created to better understand the analysis proposed, and the second, UGR’16, is a specifically designed real-traffic dataset for network security anomaly detection. We have drawn conclusions that we consider to be useful when applying generative models to network security detection.

Color deconvolution aims at separating multi-stained images into single stained ones. In digital histopathological images, true stain color vectors vary between images and need to be estimated to obtain stain concentrations and separate stain bands. These band images can be used for image analysis purposes and, once normalized, utilized with other multi-stained images (from different laboratories and obtained using different scanners) for classification purposes. In this paper we propose the use of Super Gaussian (SG) priors for each stain concentration together with the similarity to a given reference matrix for the color vectors. Variational inference and an evidence lower bound are utilized to automatically estimate all the latent variables. The proposed methodology is tested on real images and compared to classical and state-of-the-art methods for histopathological blind image color deconvolution.