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

Isospin effect in the statistical sequential decay

Isospin effect of the statistical emission fragments from the equilibrated source is investigated in the frame of statistical binary decay implemented into GEMINI code, isoscaling behavior is observed and the dependences of isoscaling parameters $\alpha$ and $\beta$ on emission fragment size, source size, source isospin asymmetry and excitation energies are studied. Results show that $\alpha$ and $\beta$ neither depends on light fragment size nor on source size. A good linear dependence of $\alpha$ and $\beta$ on the inverse of temperature $T$ is manifested and the relationship of $\alpha=4C_{sym}[(Z_{s}/A_{s})_{1}^{2}-(Z_{s}/A_{s})_{2}^{2}]/T$ and $\beta=4C_{sym}[(N_{s}/A_{s})_{1}^{2}-(N_{s}/A_{s})_{2}^{2}]/T$ from different isospin asymmetry sources are satisfied. The symmetry energy coefficient $C_{sym}$ extracted from simulation results is $\sim$ 23 MeV which includes both the volume and surface term contributions, of which the surface effect seems to play a significant role in the symmetry energy.Comment: 8 pages, 8 figures; A new substantially modified version which has been accepted by the Physical Review

Good Practice in CNN Feature Transfer

The objective of this paper is the effective transfer of the Convolutional Neural Network (CNN) feature in image search and classification. Systematically, we study three facts in CNN transfer. 1) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one. 2) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps. Our observation suggests that the Conv5 feature yields very competitive accuracy under such pooling step. 3) We find that the simple combination of pooled features extracted across various CNN layers is effective in collecting evidences from both low and high level descriptors. Following these good practices, we are capable of improving the state of the art on a number of benchmarks to a large margin

Dynamical and sequential decay effects on isoscaling and density dependence of the symmetry energy

The isoscaling properties of the primary and final products are studied via isospin dependent quantum molecular dynamics (IQMD) model and the followed sequential decay model GEMINI, respectively. It is found that the isoscaling parameters $\alpha$ of both primary and final products keep no significant change for light fragments, but increases with the mass for intermediate and heavy products. The dynamical effects on isoscaling are exhibited by that $\alpha$ value decreases a little with the evolution time of the system, and opposite trend for the heavy products. The secondary decay effects on isoscaling are reflected in the increasing of the $\alpha$ value for the final products which experiences secondary decay process. Furthermore the density dependence of the symmetry energy has also been explored, it is observed that in the low densities the symmetry energy coefficient has the form of $C_{sym}(\rho)\sim C_{0}(\rho/\rho_{0})^{\gamma}$, where $\gamma = 0.7 \sim 1.3$ for both primary and final products, but $C_{0}$ have different values for primary and final products. It is also suggested that it might be more reasonable to describe the density dependence of the symmetry energy coefficient by the $C_{sym}(\rho/\rho_{0})\approx C_{1}(\rho/\rho_{0})^{\gamma_{soft}} + C_{2}(\rho/\rho_{0})^{\gamma_{stiff}}$ with $\gamma_{soft}\leq 1$, $\gamma_{stiff}\geq 1$ and $C_{1}, C_{2}$ constant parameters.Comment: 10 pages, 10 figure