1,799 research outputs found
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
Ganoderma lucidum Protects Dopaminergic Neuron Degeneration through Inhibition of Microglial Activation
Abundant evidence has suggested that neuroinflammation participates in the pathogenesis of Parkinson's disease (PD). The emerging evidence has supported that microglia may play key roles in the progressive neurodegeneration in PD and might be a promising therapeutic target. Ganoderma lucidum (GL), a traditional Chinese medicinal herb, has been shown potential neuroprotective effects in our clinical trials that make us to speculate that it might possess potent anti-inflammatory and immunomodulating properties. To test this hypothesis, we investigated the potential neuroprotective effect of GL and possible underlying mechanism of action through protecting microglial activation using co-cultures of dopaminergic neurons and microglia. The microglia is activated by LPS and MPP+-treated MES 23.5 cell membranes. Meanwhile, GL extracts significantly prevent the production of microglia-derived proinflammatory and cytotoxic factors [nitric oxide, tumor necrosis factor-α (TNF-α), interlukin 1β (IL-1β)] in a dose-dependent manner and down-regulate the TNF-α and IL-1β expressions on mRNA level as well. In conclusion, our results support that GL may be a promising agent for the treatment of PD through anti-inflammation
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