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Stains, as one of most common degradations of paper cultural relics, not only affect paintings’ appearance, but sometimes even cover the text, patterns, and colors contained in the relics. Virtual restorations based on common red–green–blue images (RGB) which remove the degradations and then fill the lacuna regions with the image’s known parts with the inpainting technology could produce a visually plausible result. However, due to the lack of information inside the degradations, they always yield inconsistent structures when stains cover several color materials. To effectively remove the stains and restore the covered original contents of Chinese paintings, a novel method based on Poisson editing is proposed by exploiting the information inside the degradations of selected three feature bands as the auxiliary information to guide the restoration since the selected feature bands captured fewer stains and could expose the covered information. To make the Poisson editing suitable for stain removal, the feature bands were also exploited to search for the optimal patch for the pixels in the stain region, and the searched patch was used to construct the color constraint on the original Poisson editing to ensure the restoration of the original color of paintings. Specifically, this method mainly consists of two steps: feature band selection from hyperspectral data by establishing rules and reconstruction of stain contaminated regions of RGB image with color constrained Poisson editing. Four Chinese paintings (‘Fishing’, ‘Crane and Banana’, ‘the Hui Nationality Painting’, and ‘Lotus Pond and Wild Goose’) with different color materials were used to test the performance of the proposed method. Visual results show that this method can effectively remove or dilute the stains while restoring a painting’s original colors. By comparing values of restored pixels with nonstained pixels (reference of their same color materials), images processed by the proposed method had the lowest average root mean square error (RMSE), normalized absolute error (NAE), and average differences (AD), which indicates that it is an effective method to restore the stains of Chinese paintings.

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A novel gadolinium(III) complex with N-(2-propionic acid)-salicyloyl hydrazone (C 10 H 10 N 2 O 4 , H 3 L) and 1,10-phenanthroline (C 12 H 8 N 2 , phen) was prepared and characterized. The crystal structure of [Gd(H 2 L)(HL)-(H 2 O) 3 ] 2 •phen•4H 2 O was determined by X-ray single crystal diffractometry. The crystal complex crystallizes in the triclinic system with space group P-1. There are two 9-coordinated structure units in every complex molecule, in which Gd(III) is coordinated by two tridentate ligands (H 2 L and HL forms) and three water molecules and both the structural units take a distorted monocapped square antiprism. There is a free 1,10-phenanthroline connecting with coordinated water by hydrogen bonds in the crystal. The bioactivity tests indicate that the complex is an inhibitor against three bacteria.

Key indicators: single-crystal X-ray study; T = 298 K; mean (C-C) = 0.005 Å; R factor = 0.045; wR factor = 0.091; data-to-parameter ratio = 11.8.The title compound, [Cu(C 10 H 9 N 2 O 4 ) 2 ]Á3H 2 O, was obtained by evaporation of an aqueous ethanol solution of 2-[2-(2hydroxybenzoyl)hydrazono]propanoic acid and cupric chloride. Each Cu II atom is six-coordinated in a distorted octahedral geometry by carboxylate and acyl O atoms and imide N atoms from two tridentate ligands in the keto form, forming two five-membered rings sharing one edge for each ligand. N-HÁ Á ÁO and O-HÁ Á ÁO hydrogen bonds between water molecules and the ligands result in the formation of a three-dimensional network. ) 2 ]Á3H 2 O M r = 559.97 Triclinic, P1 a = 9.3787 (12) Å b = 10.7935 (14) Å c = 11.8795 (15) Å = 86.447 (2) = 81.805 (2) = 79.847 (2) V = 1170.8 (3) Å 3 Z = 2 Mo K radiation = 1.00 mm À1 T = 298 (2) K 0.32 Â 0.27 Â 0.14 mm Data collection Bruker SMART APEX CCD areadetector diffractometer Absorption correction: multi-scan (SAINT-Plus; Bruker, 2001) T min = 0.740, T max = 0.871 5974 measured reflections 4104 independent reflections 3173 reflections with I > 2(I) R int = 0.017