Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin

Undersampling reconstruction in parallel and single coil imaging with COMPaS -- COnvolutional Magnetic Resonance Image Prior with Sparsity regularization

Purpose: To propose COMPaS, a learning-free Convolutional Network, that combines Deep Image Prior (DIP) with transform-domain sparsity constraints to reconstruct undersampled Magnetic Resonance Imaging (MRI) data without previous training of the network. Methods: COMPaS uses a U-Net as DIP for undersampledMRdata in the image domain. Reconstruction is constrained by data fidelity to k-space measurements and transform-domain sparsity, such as Total Variation (TV) or Wavelet transform sparsity. Two-dimensional MRI data from the public FastMRI dataset with Cartesian undersampling in phase-encoding direction were reconstructed for different acceleration rates (R) from R = 2 to R = 8 for single coil and multicoil data. Performance of the proposed architecture was compared to Parallel Imaging with Compressed Sensing (PICS). Results: COMPaS outperforms standard PICS algorithms by reducing ghosting artifacts and yielding higher quantitative reconstruction quality metrics in multicoil imaging settings and especially in single coil k-space reconstruction. Furthermore, COMPaS can reconstruct multicoil data without explicit knowledge of coil sensitivity profiles. Conclusion: COMPaS utilizes a training-free convolutional network as a DIP in MRI reconstruction and transforms it with transform-domain sparsity regularization. It is a competitive algorithm for parallel imaging and a novel tool for accelerating single coil MRI.Comment: 13 pages, 8 figures, 2 table

The structure and global distribution of the endoplasmic reticulum network is actively regulated by lysosomes

The endoplasmic reticulum (ER) comprises morphologically and functionally distinct domains, sheets and interconnected tubules. These domains undergo dynamic reshaping, in response to changes in the cellular environment. However, the mechanisms behind this rapid remodeling are largely unknown. Here, we report that ER remodeling is actively driven by lysosomes, following lysosome repositioning in response to changes in nutritional status: the anchorage of lysosomes to ER growth tips is critical for ER tubule elongation and connection. We validate this causal link via the chemo- and optogenetically driven re-positioning of lysosomes, which leads to both a redistribution of the ER tubules and its global morphology. Therefore, lysosomes sense metabolic change in the cell and regulate ER tubule distribution accordingly. Dysfunction in this mechanism during axonal extension may lead to axonal growth defects. Our results demonstrate a critical role of lysosome-regulated ER dynamics and reshaping in nutrient responses and neuronal development.This research was funded by Infinitus (China) Company Ltd. (supporting M.L. and C.F.K.); a UKRI Engineering and Physical Sciences Research Council (EPSRC) grant (EP/L015889/1) awarded to the Centre for Doctoral Training in Sensor Technologies and Applications (supporting F.W.v.T.); a Sir Henry Wellcome Postdoctoral Fellowship from the Wellcome Trust (215943/Z/19/Z, to J.Q.L.); the Netherlands Organization for Scientific Research (NWO) (supporting W.N.); the European Research Council (ERC) (supporting L.K.); Wellcome Trust Collaborative Grant (203249/Z/16/Z to C.E.H. and C.F.K.); and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd., funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK (supporting E.A. and C.F.K.). D.H.’s research is supported by a PSL-Cambridge grant and an ERC grant, agreement no. 88267

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system