Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

Wiener–Khinchin Theorem in a Reverberation Chamber

IEEE The use of the Wiener–Khinchin theorem in the reverberation chamber reveals the relationships between a number of important parameters—the coherence bandwidth and the Q-factor measured in the time domain, the coherence time and the Q-factor measured in the frequency domain, the K-factor and the Doppler spectrum, and the K-factor and the total scattering cross section. The lower bound of the average K-factor is also given. Different physical quantities, which share similar mathematical insights, are unified. Analytical derivations are given, and results are validated by measurements

Copper(II) Can Kinetically Trap Arctic and Italian Amyloid‑β40 as Toxic Oligomers, Mimicking Cu(II) Binding to Wild-Type Amyloid‑β42: Implications for Familial Alzheimer’s Disease

The self-association of amyloid-β (Aβ) peptide into neurotoxic oligomers is believed to be central to Alzheimer’s disease (AD). Copper is known to impact Aβ assembly, while disrupted copper homeostasis impacts phenotype in Alzheimer’s models. Here we show the presence of substoichiometric Cu(II) has very different impacts on the assembly of Aβ40 and Aβ42 isoforms. Globally fitting microscopic rate constants for fibril assembly indicates copper will accelerate fibril formation of Aβ40 by increasing primary nucleation, while seeding experiments confirm that elongation and secondary nucleation rates are unaffected by Cu(II). In marked contrast, Cu(II) traps Aβ42 as prefibrillar oligomers and curvilinear protofibrils. Remarkably, the Cu(II) addition to preformed Aβ42 fibrils causes the disassembly of fibrils back to protofibrils and oligomers. The very different behaviors of the two Aβ isoforms are centered around differences in their fibril structures, as highlighted by studies of C-terminally amidated Aβ42. Arctic and Italian familiar mutations also support a key role for fibril structure in the interplay of Cu(II) with Aβ40/42 isoforms. The Cu(II) dependent switch in behavior between nonpathogenic Aβ40 wild-type and Aβ40 Arctic or Italian mutants suggests heightened neurotoxicity may be linked to the impact of physiological Cu(II), which traps these familial mutants as oligomers and curvilinear protofibrils, which cause membrane permeability and Ca(II) cellular influx

Charge-stripe order in the electronic ferroelectric LuFe2O4

The structural features of the charge ordering states in LuFe2O4 are characterized by in-situ cooling TEM observations from 300K down to 20K. Two distinctive structural modulations, a major q1= (1/3, 1/3, 2) and a weak q2=q1/10 + (0, 0, 3/2), have been well determined at the temperature of 20K. Systematic analysis demonstrates that the charges at low temperatures are well crystallized in a charge stripe phase, in which the charge density wave behaviors in a non-sinusoidal fashion resulting in elemental electric dipoles for ferroelectricity. It is also noted that the charge ordering and ferroelectric domains often change markedly with lowering temperatures and yields a rich variety of structural phenomena.Comment: 15 pages, 4 figure