Learning Diverse Tone Styles for Image Retouching

Image retouching, aiming to regenerate the visually pleasing renditions of given images, is a subjective task where the users are with different aesthetic sensations. Most existing methods deploy a deterministic model to learn the retouching style from a specific expert, making it less flexible to meet diverse subjective preferences. Besides, the intrinsic diversity of an expert due to the targeted processing on different images is also deficiently described. To circumvent such issues, we propose to learn diverse image retouching with normalizing flow-based architectures. Unlike current flow-based methods which directly generate the output image, we argue that learning in a style domain could (i) disentangle the retouching styles from the image content, (ii) lead to a stable style presentation form, and (iii) avoid the spatial disharmony effects. For obtaining meaningful image tone style representations, a joint-training pipeline is delicately designed, which is composed of a style encoder, a conditional RetouchNet, and the image tone style normalizing flow (TSFlow) module. In particular, the style encoder predicts the target style representation of an input image, which serves as the conditional information in the RetouchNet for retouching, while the TSFlow maps the style representation vector into a Gaussian distribution in the forward pass. After training, the TSFlow can generate diverse image tone style vectors by sampling from the Gaussian distribution. Extensive experiments on MIT-Adobe FiveK and PPR10K datasets show that our proposed method performs favorably against state-of-the-art methods and is effective in generating diverse results to satisfy different human aesthetic preferences. Source code and pre-trained models are publicly available at https://github.com/SSRHeart/TSFlow

Atomically precise M15 (M = Au/Ag/Cu) alloy nanoclusters: Structural analysis, optical and electrocatalytic CO2 reduction properties

Herein, the overall structure of a nanocluster coprotected by phosphine and mercaptan ligands [Au7Ag8(SPh)6((p-OMePh)3P)8]NO3 (Au7Ag8) was reported. For comparison, a previously reported nanocluster with the same structure, but a different metal composition, [Au13Cu2(TBBT)6((p-ClPh)3P)8]SbF6 (Au13Cu2), was synthesized. In addition, their optical and electrocatalytic CO2 reduction properties were comprehensively compared. The results reveal that the photoluminescence quantum yield (PLQY) of the Ag-doped Au7Ag8 nanocluster is 1.62%, which is seven times greater than that of the Cu-doped Au13Cu2 nanocluster (PLQY = 0.23%). Furthermore, the Au13Cu2 nanocluster demonstrates significantly enhanced catalytic selectivity for CO, with a CO Faradaic efficiency ranging from 79.7% to 90.4%, compared with that of the Au7Ag8 nanocluster (CO Faradaic efficiency: 67.2%–77.7%) within a potential range of 0.5 to −1.1 V. From structural analyses, the superior CO selectivity of Au13Cu2 is attributed to the copper dopant

Metasurface-based Mueller Matrix Microscope

In conventional optical microscopes, image contrast of objects mainly results from the differences in light intensity and/or color. Muller matrix optical microscopes (MMMs), on the other hand, can provide significantly enhanced image contrast and rich information about objects by analyzing their interactions with polarized light. However, state-of-art MMMs are fundamentally limited by bulky and slow polarization state generators and analyzers. Here, we demonstrated the feasibility of applying metasurfaces to enable a fast and compact MMM, i.e., Meta-MMM. We developed a dual-color MMM, in both reflection and transmission modes, based on a chip-integrated high-speed (>20fps) metasurface polarization state analyzer (Meta-PSA) and realized high measurement accuracy for Muller matrix (MM) imaging. We then applied our Meta-MMM to nanostructure characterization, surface morphology analysis and discovered birefringent structures in honeybee wings. Our meta-MMMs hold the promise to revolutionize various applications from biological imaging, medical diagnosis, material characterization to industry inspection and space exploration

Defects Vibrations Engineering for Enhancing Interfacial Thermal Transport

To push upper boundaries of effective thermal conductivity in polymer composites, a fundamental understanding of thermal transport mechanisms is crucial. Although there is intensive simulation research, systematic experimental investigation on thermal transport in polymer composites is limited. To better understand thermal transport processes, we design polymer composites with perfect fillers (graphite) and defective fillers (graphite oxide); we choose polar polyvinyl alcohol (PVA) as a matrix model; and we identify how thermal transport occurs across heterogeneous interfaces. Measured thermal conductivities of in PVA/defective filler composites is higher than those of PVA/perfect filler composites, while measured thermal conductivities in defective fillers are lower than those of perfect fillers. An effective quantum mechanical model is developed, showing that the vibrational state of the defective level plays a critical role in enhancing the thermal conductivity with increased defect concentration. Our experimental and model results have suggested that defects in polymer composites may enhance thermal transport in polymer composites by promoting vibrational resonant couplings.Comment: Enclosed: (i) Main Manuscript, including 5 main figures. (ii) Supplementary Information, including 16 Supplementary Figures and one self-contained theoretical sectio