Neural Degradation Representation Learning for All-In-One Image Restoration

Existing methods have demonstrated effective performance on a single degradation type. In practical applications, however, the degradation is often unknown, and the mismatch between the model and the degradation will result in a severe performance drop. In this paper, we propose an all-in-one image restoration network that tackles multiple degradations. Due to the heterogeneous nature of different types of degradations, it is difficult to process multiple degradations in a single network. To this end, we propose to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations. The learned NDR decomposes different types of degradations adaptively, similar to a neural dictionary that represents basic degradation components. Subsequently, we develop a degradation query module and a degradation injection module to effectively recognize and utilize the specific degradation based on NDR, enabling the all-in-one restoration ability for multiple degradations. Moreover, we propose a bidirectional optimization strategy to effectively drive NDR to learn the degradation representation by optimizing the degradation and restoration processes alternately. Comprehensive experiments on representative types of degradations (including noise, haze, rain, and downsampling) demonstrate the effectiveness and generalization capability of our method

Mutual-Guided Dynamic Network for Image Fusion

Image fusion aims to generate a high-quality image from multiple images captured under varying conditions. The key problem of this task is to preserve complementary information while filtering out irrelevant information for the fused result. However, existing methods address this problem by leveraging static convolutional neural networks (CNNs), suffering two inherent limitations during feature extraction, i.e., being unable to handle spatial-variant contents and lacking guidance from multiple inputs. In this paper, we propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs. Specifically, we design a mutual-guided dynamic filter (MGDF) for adaptive feature extraction, composed of a mutual-guided cross-attention (MGCA) module and a dynamic filter predictor, where the former incorporates additional guidance from different inputs and the latter generates spatial-variant kernels for different locations. In addition, we introduce a parallel feature fusion (PFF) module to effectively fuse local and global information of the extracted features. To further reduce the redundancy among the extracted features while simultaneously preserving their shared structural information, we devise a novel loss function that combines the minimization of normalized mutual information (NMI) with an estimated gradient mask. Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks. The code and model are publicly available at: https://github.com/Guanys-dar/MGDN.Comment: ACMMM 2023 accepte

Single-step chemical vapour deposition of anti-pyramid MoS2/WS2vertical heterostructures

| openaire: EC/H2020/820423/EU//S2QUIP | openaire: EC/H2020/834742/EU//ATOPVan der Waals heterostructures are the fundamental building blocks of electronic and optoelectronic devices. Here we report that, through a single-step chemical vapour deposition (CVD) process, high-quality vertical bilayer MoS2/WS2 heterostructures with a grain size up to ∼60 μm can be synthesized from molten salt precursors, Na2MoO4 and Na2WO4. Instead of normal pyramid vertical heterostructures grown by CVD, this method synthesizes an anti-pyramid MoS2/WS2 structure, which is characterized by Raman, photoluminescence and second harmonic generation microscopy. Our facile CVD strategy for synthesizing anti-pyramid structures unveils a new synthesis route for the products of two-dimensional heterostructures and their devices for application.Peer reviewe

ZnO-Assisted Growth of CH₃NH₃PbI_3–<i>x</i>Cl_<i>x</i> Film and Efficient Planar Perovskite Solar Cells with a TiO₂/ZnO/C₆₀ Electron Transport Trilayer

Appropriate electron transport layers (ETL) are essential in perovskite solar cells (PSCs) with high power conversion efficiency (PCE). Herein, a TiO₂/ZnO/C₆₀ trilayer fabricated on a transparent fluorine-doped tin oxide (FTO) glass substrate is used as a compound ETL in planar PSCs. The trilayer shows positive effects on both perovskite synthesis and device performance. The ZnO layer assists growth of CH₃NH₃PbI_3–<i>x</i>Cl_<i>x</i> (<i>x</i> ≈ 0) annealed at a lower temperature and with a shorter time, which is due to a more rapid and easier decomposition of the intermediate CH₃NH₃PbCl₃ phase in the growth of CH₃NH₃PbI_3–<i>x</i>Cl_<i>x</i>. All three materials in the trilayer are important for obtaining PSCs with a high PCE. ZnO is critical for enhancing the open circuit voltage by ensuring proper energy alignment with the TiO₂ and C₆₀ layers. C₆₀ enhances carrier extraction from the CH₃NH₃PbI_3–<i>x</i>Cl_<i>x</i> layer. TiO₂ eliminates charge recombination at the FTO surface and ensures efficient electron collection. The best-performing PSC based on the TiO₂/ZnO/C₆₀ electron transport trilayer features a PCE of 18.63% with a fill factor of 79.12%. These findings help develop an understanding of the effects of ZnO-containing ETLs on perovskite film synthesis and show promise for the future development of high-performance PSCs with compound ETLs

Solid-Phase Coalescence of Electrochemically Exfoliated Graphene Flakes into a Continuous Film on Copper

The ability to directly synthesize high-quality graphene patterns over large areas is important for many applications such as electronic and optoelectronic devices and circuits. Here, we report a facile and scalable approach to coalesce and recrystallize electrochemically exfoliated graphene flakes into a continuous film by thermal annealing on copper foils. The underlying growth mechanism involves defect mediated decomposition of electrochemically exfoliated graphene flakes into active polycyclic carbon species, followed by coalescence of the active carbon species into a continuous, monolayer film of high material quality. First-principles calculations confirm that the enhanced affinity of the polycyclic carbon species with copper effectively prevents their surface desorption at elevated temperatures, which is distinct from graphene growth based on the decomposition of solid carbon sources into gaseous hydrocarbons. Significantly, the localized supply of active carbon species in our approach enables spatially confined growth of graphene. Combined with stencil printing of the exfoliated flakes, transparent and conductive graphene circuits have been directly synthesized over large areas

A genetic variant in the promoter region of miR-106b-25 cluster predict clinical outcome of HBV-related hepatocellular carcinoma in Chinese.

BACKGROUND: MiR-106b-25 cluster, hosted in intron 13 of MCM7, may play integral roles in diverse processes including immune response, tumorigenesis and progression. A single nucleotide polymorphism (SNP), rs999885, is located in the promoter region of MCM7. Our previous study showed that the A to G base change of rs999885 may provide an increased risk for HCC in HBV persistent carriers by altering the expression of the miR-106b-25 cluster. However, it is unknown whether rs999885 is associated with prognosis of intermediate or advanced HBV-related hepatocellular carcinoma (HCC) patients. METHODS: The SNP, rs999885, was genotyped by using the TaqMan allelic discrimination Assay in 414 intermediate or advanced HCC patients. Log-rank test and Cox proportional hazard models were used for survival analysis. RESULTS: The variant genotypes of rs999885 were associated with a significantly decreased risk of death for intermediate or advanced HCC [additive model: adjusted hazard ratio (HR)  = 0.76,95% confidence intervals (CI)  = 0.59-0.97]. Further stepwise regression analysis suggested that rs999885 was an independently protective factor for the prognosis of HCC in the final model (additive model: adjusted HR  = 0.72, 95% CI  = 0.56-0.91, P = 0.007). CONCLUSIONS: These findings indicate that the A to G base change of rs999885 may provide a protective effect on the prognosis of intermediate or advanced HCC in Chinese

A genetic variant in primary miR-378 is associated with risk and prognosis of hepatocellular carcinoma in a Chinese population.

BACKGROUND: MiR-378 has been reported to be related to cell survival, tumor growth and angiogenesis and may participate in hepatocellular carcinoma (HCC) development and prognosis. Genetic variants in primary miR-378 (pri-miR-378) may impact miR-378 expression and contribute to HCC risk and survival. This study aimed to assess the associations between a genetic variant in primary miR-378 and HCC susceptibility and prognosis. METHODS: We conducted a case-control study to analyze the association of rs1076064 in pri-miR-378 with hepatocellular carcinoma risk in 1300 HCC patients with positive hepatitis B virus (HBV) and 1344 HBV carriers. Then, we evaluated the correlation between the polymorphism and hepatocellular carcinoma prognosis in 331 HCC patients at either intermediate or advanced stage without surgical treatment. RESULTS: The variant genotypes of rs1076064 were associated with a decreased HCC risk in HBV carriers [Adjusted odds ratio (OR) = 0.90, 95% confidence intervals (CI) = 0.81-1.00, P = 0.047]. Moreover, HCC patients with the variant genotypes were associated with a better survival [Adjusted hazard ratio (HR) = 0.70, 95% CIs = 0.59-0.83, P<0.0001 in an additive genetic model]. The reporter gene assay showed that the variant G allele of rs1076064 exerted higher promoter activity than the A allele. CONCLUSIONS: These findings indicate that rs1076064 may be a biomarker for HCC susceptibility and prognosis through altering pri-miR-378 transcription