Infrared Image Super-Resolution: Systematic Review, and Future Trends

Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a continuing concern within the development of deep learning. This survey aims to provide a comprehensive perspective of IR image super-resolution, including its applications, hardware imaging system dilemmas, and taxonomy of image processing methodologies. In addition, the datasets and evaluation metrics in IR image super-resolution tasks are also discussed. Furthermore, the deficiencies in current technologies and possible promising directions for the community to explore are highlighted. To cope with the rapid development in this field, we intend to regularly update the relevant excellent work at \url{https://github.com/yongsongH/Infrared_Image_SR_SurveyComment: Submitted to IEEE TNNL

Target-oriented Domain Adaptation for Infrared Image Super-Resolution

Recent efforts have explored leveraging visible light images to enrich texture details in infrared (IR) super-resolution. However, this direct adaptation approach often becomes a double-edged sword, as it improves texture at the cost of introducing noise and blurring artifacts. To address these challenges, we propose the Target-oriented Domain Adaptation SRGAN (DASRGAN), an innovative framework specifically engineered for robust IR super-resolution model adaptation. DASRGAN operates on the synergy of two key components: 1) Texture-Oriented Adaptation (TOA) to refine texture details meticulously, and 2) Noise-Oriented Adaptation (NOA), dedicated to minimizing noise transfer. Specifically, TOA uniquely integrates a specialized discriminator, incorporating a prior extraction branch, and employs a Sobel-guided adversarial loss to align texture distributions effectively. Concurrently, NOA utilizes a noise adversarial loss to distinctly separate the generative and Gaussian noise pattern distributions during adversarial training. Our extensive experiments confirm DASRGAN's superiority. Comparative analyses against leading methods across multiple benchmarks and upsampling factors reveal that DASRGAN sets new state-of-the-art performance standards. Code are available at \url{https://github.com/yongsongH/DASRGAN}.Comment: 11 pages, 9 figure

The moderating effect of psychological trust on knowledge spillovers and firms’ open innovation

Psychological trust is an important link in building interpersonal relationships and has a significant impact on the attitude and behavior of knowledge subjects. Based on the characteristics of knowledge attributes, this paper analyzed the data of 180 high-tech firms in China from 2014 to 2020 to deeply explore the effects of explicit knowledge spillover and tacit knowledge spillover on firms’ open innovation, and the moderating effect of psychological trust on the relationship between the two. It is found that: first, explicit knowledge spillover and tacit knowledge spillover have an inverted U-shaped relationship with firms’ open innovation, i.e., the effect of open innovation increases and then decreases as the degree of knowledge spillover increases; second, psychological trust positively moderates the non-linear relationship between knowledge spillover and firms’ open innovation. This paper provides a rational explanation of firms’ management behavior from a psychological perspective, and enriches and expands the research related to knowledge spillover, firms’ open innovation and psychological trust. It is suggested that firms should pay more attention to inter-organizational trust relationships and pay attention to the psychological growth and development of knowledge employees to improve open innovation in firms

Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation

Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. Though data augmentation has been a proven technique to boost the generalization capabilities of conventional centralized DL as a "free lunch", its application in FL is largely underexplored. Notably, constrained by costly labeling, 3D medical segmentation generally relies on data augmentation. In this work, we aim to develop a vicinal feature-level data augmentation (VFDA) scheme to efficiently alleviate the local feature shift and facilitate collaborative training for privacy-aware FL segmentation. We take both the inner- and inter-institute divergence into consideration, without the need for cross-institute transfer of raw data or their mixup. Specifically, we exploit the batch-wise feature statistics (e.g., mean and standard deviation) in each institute to abstractly represent the discrepancy of data, and model each feature statistic probabilistically via a Gaussian prototype, with the mean corresponding to the original statistic and the variance quantifying the augmentation scope. From the vicinal risk minimization perspective, novel feature statistics can be drawn from the Gaussian distribution to fulfill augmentation. The variance is explicitly derived by the data bias in each individual institute and the underlying feature statistics characterized by all participating institutes. The added-on VFDA consistently yielded marked improvements over six advanced FL methods on both 3D brain tumor and cardiac segmentation.Comment: 28th biennial international conference on Information Processing in Medical Imaging (IPMI 2023): Oral Pape

Production and temperature sensitivity of long chain alkenones in the cultured haptophyte Pseudoisochrysis paradoxa

The alkenone unsaturation index (U<sub>37</sub><sup>K</sup> or U<sub>37</sub><sup>K′</sup>) serves as a critical tool for reconstructing temperature in marine environments. Lacustrine haptophyte algae are genetically distinct from their ubiquitous and well studied marine counterparts, and the unknown species-specific genetic imprints on long chain alkenone production by lacustrine species have hindered the widespread application of the U37<sup>K</sup> temperature proxy to lake sediment records. The haptophyte Pseudoisochrysis paradoxa produces alkenones but its U37<sup>K</sup> calibration has never been determined. It has an alkenone fingerprint abundant in tetraunsaturated alkenones, a hallmark of lacustrine environments. We present here the first calibration of the U37<sup>K</sup> index to temperature for a culture of P. paradoxa. We found that the U37<sup>K</sup> index accurately captured the alkenone response to temperature whereas the U37<sup>K′</sup> index failed to do so, with U37<sup>K′</sup> values below 0.08 projecting to two different temperature values. Our results add a fifth species-specific U37<sup>K</sup> calibration and provide another line of evidence that different haptophyte species require different U37<sup>K</sup> calibrations. The findings also highlight the necessary inclusion of the C<sub>37:4</sub> alkenone when reconstructing temperatures from P. paradoxa-derived alkenone records

Widespread occurrence of distinct alkenones from Group I haptophytes in freshwater lakes: Implications for paleotemperature and paleoenvironmental reconstructions

Alkenones are C35-C42 polyunsaturated ketone lipids that are commonly employed to reconstruct changes in sea surface temperature. However, their use in coastal seas and saline lakes can be hindered by species-mixing effects. We recently hypothesized that freshwater lakes are immune to species-mixing effects because they appear to exclusively host Group I haptophyte algae, which produce a distinct distribution of alkenones with a relatively consistent response of alkenone unsaturation to temperature. To evaluate this hypothesis and explore the geographic extent of Group I haptophytes, we analyzed alkenones in sediment and suspended particulate matter samples from lakes distributed throughout the mid- and high latitudes of the Northern Hemisphere (n = 30). Our results indicate that Group I-type alkenone distributions are widespread in freshwater lakes from a range of different climates (mean annual air temperature range: -17.3-10.9 degrees C; mean annual precipitation range: 125-1657 mm yr(-1); latitude range: 40-81 degrees N), and are commonly found in neutral to basic lakes (pH > 7.0), including volcanic lakes and lakes with mafic bedrock. We show that these freshwater lakes do not feature alkenone distributions characteristic of Group II lacustrine haptophytes, providing support for the hypothesis that freshwater lakes are immune to species-mixing effects. In lakes that underwent temporal shifts in salinity, we observed mixed Group I/II alkenone distributions and the alkenone contributions from each group could be quantified with the RIK37 index. Additionally, we observed significant correlations of alkenone unsaturation (U-37(K)) with seasonal and mean annual air temperature with this expanded freshwater lakes dataset, with the strongest correlation occurring during the spring transitional season (U-37(K) = 0.029 * T - 0.49; r(2) = 0.60; p < 0.0001). We present new sediment trap data from two lakes in northern Alaska (Toolik Lake, 68.632 degrees N, 149.602 degrees W; lake E5, 68.643 degrees N, 149.458 degrees W) that demonstrate the highest sedimentary fluxes of alkenones in the spring transitional season, concurrent with the period of lake ice melt and isothermal mixing. Together, these data provide a framework for evaluating lacustrine alkenone distributions and utilizing alkenone unsaturation as a lake temperature proxy. (C) 2018 Elsevier B.V. All rights reserved

Crystal Structure of TDRD3 and Methyl-Arginine Binding Characterization of TDRD3, SMN and SPF30

SMN (Survival motor neuron protein) was characterized as a dimethyl-arginine binding protein over ten years ago. TDRD3 (Tudor domain-containing protein 3) and SPF30 (Splicing factor 30 kDa) were found to bind to various methyl-arginine proteins including Sm proteins as well later on. Recently, TDRD3 was shown to be a transcriptional coactivator, and its transcriptional activity is dependent on its ability to bind arginine-methylated histone marks. In this study, we systematically characterized the binding specificity and affinity of the Tudor domains of these three proteins quantitatively. Our results show that TDRD3 preferentially recognizes asymmetrical dimethylated arginine mark, and SMN is a very promiscuous effector molecule, which recognizes different arginine containing sequence motifs and preferentially binds symmetrical dimethylated arginine. SPF30 is the weakest methyl-arginine binder, which only binds the GAR motif sequences in our library. In addition, we also reported high-resolution crystal structures of the Tudor domain of TDRD3 in complex with two small molecules, which occupy the aromatic cage of TDRD3