Universal Denoising Networks : A Novel CNN Architecture for Image Denoising

We design a novel network architecture for learning discriminative image models that are employed to efficiently tackle the problem of grayscale and color image denoising. Based on the proposed architecture, we introduce two different variants. The first network involves convolutional layers as a core component, while the second one relies instead on non-local filtering layers and thus it is able to exploit the inherent non-local self-similarity property of natural images. As opposed to most of the existing deep network approaches, which require the training of a specific model for each considered noise level, the proposed models are able to handle a wide range of noise levels using a single set of learned parameters, while they are very robust when the noise degrading the latent image does not match the statistics of the noise used during training. The latter argument is supported by results that we report on publicly available images corrupted by unknown noise and which we compare against solutions obtained by competing methods. At the same time the introduced networks achieve excellent results under additive white Gaussian noise (AWGN), which are comparable to those of the current state-of-the-art network, while they depend on a more shallow architecture with the number of trained parameters being one order of magnitude smaller. These properties make the proposed networks ideal candidates to serve as sub-solvers on restoration methods that deal with general inverse imaging problems such as deblurring, demosaicking, superresolution, etc.Comment: Camera ready paper to appear in the Proceedings of CVPR 201

3D Poisson microscopy deconvolution with Hessian Schatten-norm regularization

Inverse problems with shot noise arise in many modern biomedical imaging applications. The main challenge is to obtain an estimate of the underlying specimen from measurements corrupted by Poisson noise. In this work, we propose an efficient framework for photon-limited image reconstruction, under a regularization approach that relies on matrix-valued operators. Our regularizers involve the Hessian operator and its eigenvalues. They are second-order regularizers that are well suited to biomedical images. For the solution of the arising minimization problem, we propose an optimization algorithm based on an augmented-Lagrangian formulation and specifically tailored to the Poisson nature of the noise. To assess the quality of the reconstruction, we provide experimental results on 3D image stacks of biological images for microscopy deconvolution

Structure tensor total variation

This is the final version of the article. Available from Society for Industrial and Applied Mathematics via the DOI in this record.We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure tensor and is suitable for both grayscale and vector-valued images. It generalizes several existing variational penalties, including the total variation seminorm and vectorial extensions of it. Meanwhile, thanks to the structure tensor’s ability to capture first-order information around a local neighborhood, the STV functionals can provide more robust measures of image variation. Further, we prove that the STV regularizers are convex while they also satisfy several invariance properties w.r.t. image transformations. These properties qualify them as ideal candidates for imaging applications. In addition, for the discrete version of the STV functionals we derive an equivalent definition that is based on the patch-based Jacobian operator, a novel linear operator which extends the Jacobian matrix. This alternative definition allow us to derive a dual problem formulation. The duality of the problem paves the way for employing robust tools from convex optimization and enables us to design an efficient and parallelizable optimization algorithm. Finally, we present extensive experiments on various inverse imaging problems, where we compare our regularizers with other competing regularization approaches. Our results are shown to be systematically superior, both quantitatively and visually

The basics of mitochondrial cAMP signalling: Where, when, why

Cytosolic cAMP signalling in live cells has been extensively investigated in the past, while only in the last decade the existence of an intramitochondrial autonomous cAMP homeostatic system began to emerge. Thanks to the development of novel tools to investigate cAMP dynamics and cAMP/PKA-dependent phosphorylation within the matrix and in other mitochondrial compartments, it is now possible to address directly and in intact living cells a series of questions that until now could be addressed only by indirect approaches, in isolated organelles or through subcellular fractionation studies. In this contribution we discuss the mechanisms that regulate cAMP dynamics at the surface and inside mitochondria, and its crosstalk with organelle Ca2+ handling. We then address a series of still unsolved questions, such as the intramitochondrial localization of key elements of the cAMP signaling toolkit, e.g., adenylate cyclases, phosphodiesterases, protein kinase A (PKA) and Epac. Finally, we discuss the evidence for and against the existence of an intramitochondrial PKA pool and the functional role of cAMP increases within the organelle matrix