Information Purification Network for Remote Sensing Image Super-Resolution

Recently,several well-performing deep convolutional neural networks were proposed for remote sensing image super-resolution(SR).However,these methods rarely consider that remote sensing images are corruptible by additional noise,blurring,and other factors.Therefore,to eliminate the interference of these factors,especially the noise,we propose a novel information purification network(IPN)for remote sensing image SR.The proposed information purification block(IPB)can process channel-wise features differently by channel separation and rescale spatial-wise features adaptively through the proposed multi-scale spatial attention mechanism.We further design an information group to explore a more powerful expressive combination of IPBs.Moreover,long and short skip connections can transmit abundant low-frequency information,making IPBs pay more attention to high-frequency information.We mix the images under various degradation models as training data in the training phase.In this way,the network can directly reconstruct various degraded images.Experiments on AID and UC Merced Land-Use datasets under multiple degradation models demonstrate that the proposed IPN performs better than state-of-the-art methods.

RFCNet:Remote Sensing Image Super-Resolution Using Residual Feature Calibration Network

In the field of single remote sensing image Super-Resolution(SR),deep Convolutional Neural Networks(CNNs)have achieved top performance.To further enhance convolutional module performance in processing remote sensing images,we construct an efficient residual feature calibration block to generate expressive features.After harvesting residual features,we first divide them into two parts along the channel dimension.One part flows to the Self-Calibrated Convolution(SCC)to be further refined,and the other part is rescaled by the proposed Two-Path Channel Attention(TPCA)mechanism.SCC corrects local features according to their expressions under the deep receptive field,so that the features can be refined without increasing the number of calculations.The proposed TPCA uses the means and variances of feature maps to obtain accurate channel attention vectors.Moreover,a region-level nonlocal operation is introduced to capture long-distance spatial contextual information by exploring pixel dependencies at the region level.Extensive experiments demonstrate that the proposed residual feature calibration network is superior to other SR methods in terms of quantitative metrics and visual quality.

Theoretical prediction on the reactivity of the Co-mediated intramolecular Pauson-Khand reaction for constructing bicyclo-skeletons in natural products

The Co_2(CO)_8-mediated intramolecular Pauson-Khand reaction is an efficient approach for constructing polycyclic skeletons. Recently, some of us reported a series of this type reactions involving stericallyhindered enynes for synthesizing natural products with reasonable reaction rates and yields. However,the reason for the high reactivity of the reaction remains unclear. We employed density functional theory calculations to clarify the mechanism and reactivity for this reaction. In contrast with chain olefin reactants, CO insertion is considered to be the rate-determining step for the overall Pauson-Khand reaction of cyclooctene derivatives. The reduced activation free energy for the alkene insertion step is attributed to: i) the electron-withdrawing group in close proximity to the C—C triple bond enhancing the reactivity of the alkyne moiety; ii) lower steric hindrance during alkene insertion when using the cyclooctene derivative. The effect of the substituent on the Co_2(CO)_8-mediated intramolecular PausonKhand reaction was then investigated. Internal alkenes exhibit lower reactivity than terminal alkenes because of the steric hindrance introduced by the substituted group. The cis internal alkene exhibits higher reactivity than the trans internal alkene. An ester group in close proximity to the C—C triple bond significantly enhances the reactivity.