Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms.

Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances.

Tecomastane, a new megastigmane from the flowers of Tecoma stans.

Tecoma stans is a tropical plant that is widely used in folk medicine. Little is known about the chemical constituents of flowers of this plant. From flowers of the native plant in Vietnam, 12 compounds were isolated and elucidated, including one new compound tecomastane (1) and eleven known compounds, (3S,5R,6S,7E)-5,6-epoxy-3-hydroxy-7-megastigmane-9-one (2), bosciallin (3), chakyunglupulin B (4), (2S,6R)-2,6-dimethyloctane-1,8-diol (5), cleroindicin F (6), rengyoxide (7), 3,4-dihydroxybenzoic acid (8), methyl 3,4-dihydrobenzoate (9), 3,5-dihydroxybenzoic acid (10), luteolin (11), and indole-3-carboxylic acid (12). Compound 5 was a new natural product. The chemical structures of isolated compounds were identified by interpretation of their spectroscopic data (1D, 2D NMR, and HRESIMS) and by comparison with the literature. Compounds 1-7 and 10-12 were evaluated for alpha-glucosidase inhibition and antimicrobial activity against antibiotic-resistant, pathogenic bacteria Enterococcus faecium, Staphylococcus aureus, and Acinetobacter baumannii.