Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections

In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images. The constraints of tubal-rank and sparsity can govern the solution of the denoised tensor in the recovery procedure. To solve the constrained low-tubal-rank model, we develop an iterative algorithm based on bilateral random projections to efficiently solve the proposed model. The advantage of random projections is that the approximation of the low-tubal-rank tensor can be obtained quite accurately in an inexpensive manner. Experimental examples for hyperspectral image denoising are presented to demonstrate the effectiveness and efficiency of the proposed method.Comment: Accepted by IGARSS 201

2-tert-Butyl 4-methyl 3,5-dimethyl-1H-pyrrole-2,4-dicarboxyl­ate

In the title mol­ecule, C13H19NO4, except for two C atoms of the tert-butyl group, the non-H atoms are almost coplanar (r.m.s. deviation = 0.2542 Å). In the crystal, mol­ecules are linked into centrosymmetric dimers by two inter­molecular N—H⋯O hydrogen bonds, forming an R 2 2(10) ring motif

MAP3Kinase-dependent SnRK2-kinase activation is required for abscisic acid signal transduction and rapid osmotic stress response.

Abiotic stresses, including drought and salinity, trigger a complex osmotic-stress and abscisic acid (ABA) signal transduction network. The core ABA signalling components are snf1-related protein kinase2s (SnRK2s), which are activated by ABA-triggered inhibition of type-2C protein-phosphatases (PP2Cs). SnRK2 kinases are also activated by a rapid, largely unknown, ABA-independent osmotic-stress signalling pathway. Here, through a combination of a redundancy-circumventing genetic screen and biochemical analyses, we have identified functionally-redundant MAPKK-kinases (M3Ks) that are necessary for activation of SnRK2 kinases. These M3Ks phosphorylate a specific SnRK2/OST1 site, which is indispensable for ABA-induced reactivation of PP2C-dephosphorylated SnRK2 kinases. ABA-triggered SnRK2 activation, transcription factor phosphorylation and SLAC1 activation require these M3Ks in vitro and in plants. M3K triple knock-out plants show reduced ABA sensitivity and strongly impaired rapid osmotic-stress-induced SnRK2 activation. These findings demonstrate that this M3K clade is required for ABA- and osmotic-stress-activation of SnRK2 kinases, enabling robust ABA and osmotic stress signal transduction

(E,E)-3,3′-Dimethyl-1,1′-diphenyl-4,4′-{[3-aza­pentane-1,5-diylbis(aza­nedi­yl)]bis­(phenyl­methyl­idyne)}di-1H-pyrazol-5(4H)-one

The asymmetric unit of the title compound, C38H37N7O2, contains one half-mol­ecule, situated on a twofold rotational axis, in which one amino group is involved in intra­molecular N—H⋯O hydrogen bond and the two phenyl rings are twisted from the plane of pyrazolone ring by 26.69 (10) and 79.64 (8)°. The crystal packing exhibits no classical inter­molecular contacts

SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation

In this paper, we propose a scribble-based video colorization network with temporal aggregation called SVCNet. It can colorize monochrome videos based on different user-given color scribbles. It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding. To improve the colorization quality and strengthen the temporal consistency, we adopt two sequential sub-networks in SVCNet for precise colorization and temporal smoothing, respectively. The first stage includes a pyramid feature encoder to incorporate color scribbles with a grayscale frame, and a semantic feature encoder to extract semantics. The second stage finetunes the output from the first stage by aggregating the information of neighboring colorized frames (as short-range connections) and the first colorized frame (as a long-range connection). To alleviate the color bleeding artifacts, we learn video colorization and segmentation simultaneously. Furthermore, we set the majority of operations on a fixed small image resolution and use a Super-resolution Module at the tail of SVCNet to recover original sizes. It allows the SVCNet to fit different image resolutions at the inference. Finally, we evaluate the proposed SVCNet on DAVIS and Videvo benchmarks. The experimental results demonstrate that SVCNet produces both higher-quality and more temporally consistent videos than other well-known video colorization approaches. The codes and models can be found at https://github.com/zhaoyuzhi/SVCNet.Comment: accepted by IEEE Transactions on Image Processing (TIP

VCGAN: Video Colorization with Hybrid Generative Adversarial Network

We propose a hybrid recurrent Video Colorization with Hybrid Generative Adversarial Network (VCGAN), an improved approach to video colorization using end-to-end learning. The VCGAN addresses two prevalent issues in the video colorization domain: Temporal consistency and unification of colorization network and refinement network into a single architecture. To enhance colorization quality and spatiotemporal consistency, the mainstream of generator in VCGAN is assisted by two additional networks, i.e., global feature extractor and placeholder feature extractor, respectively. The global feature extractor encodes the global semantics of grayscale input to enhance colorization quality, whereas the placeholder feature extractor acts as a feedback connection to encode the semantics of the previous colorized frame in order to maintain spatiotemporal consistency. If changing the input for placeholder feature extractor as grayscale input, the hybrid VCGAN also has the potential to perform image colorization. To improve the consistency of far frames, we propose a dense long-term loss that smooths the temporal disparity of every two remote frames. Trained with colorization and temporal losses jointly, VCGAN strikes a good balance between color vividness and video continuity. Experimental results demonstrate that VCGAN produces higher-quality and temporally more consistent colorful videos than existing approaches.Comment: Submitted Major Revision Manuscript of IEEE Transactions on Multimedia (TMM

Ethyl 5-{[(E)-2-(isonicotinoyl)hydrazinyl­idene]methyl}-3,4-dimethyl-1H-pyrrole-2-carboxyl­ate dihydrate

In the title compound, C16H18N4O3·2H2O, the dihedral angle between the pyrrole and pyridine rings in the hydrazone mol­ecule is 7.12 (3)°. In the crystal structure, inter­molecular N—H⋯O, O—H⋯N and O—H⋯O hydrogen bonds link the hydrazone and water mol­ecules into double layers parallel to (101). The crystal packing exhibits weak π–π inter­actions between the pyrrole and pyridine rings of neighbouring hydrazone mol­ecules [centroid–centroid distance = 3.777 (3) Å]. The crystal studied was a non-merohedral twin, the refined ratio of twin domains being 0.73 (3):0.27 (3)

Quantum chemistry calculation aided design of chiral ionic liquid-based extraction system for amlodipine separation

Amlodipine is a widely used medication in treating hypertension, which is also known as a chiral compound. So far efforts have been made to obtain optically pure (S)-amlodipine because (R)-amlodipine has poor efficacy and is related to undesirable side effects. However, the available separation methods for amlodipine are still unsatisfactory. Recently, chiral separation has become a promising application of chiral ionic liquids (CILs), because the structural designability enables them adjustable separation efficiency for specific tasks. In this work, a high-efficient CIL-based liquid-liquid extraction system was developed for racemic amlodipine separation with the assistance of quantum chemistry calculations. Enantioselectivity up to 1.35 achieved by the novel system at 298.15 K is significantly higher than other available extraction systems. Moreover, the recycling of CIL can be easily realized by backward extraction of amlodipine, which is important for the industrial application of CILs