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Some aliphatic carboxylic acids were used to produce chitosan (CS) salts by reaction with CS, and their antifungal activity against three kinds of phytopathogens was estimated by hypha measurement in vitro. The fungicidal assessment showed that all of the CS salts had excellent activity against the tested fungi. Their inhibitory indices were 41. 15-64.15, 56.25-76.56, and 35.94-68.75% for Cladosporium cucumerinum (Ell.) et Arthur, Monilinia fructicola (Wint.) Honey, and Fusarium oxysporum sp. Cucumis sativus L., respectively, at 1000 lg/mL; these indices were higher than that of CS. It was confirmed that the amino groups' protonation was important for the antifungal activity of CS derivatives. The substituted groups with stronger electronegativity drew more electrons from the nitrogen atoms in the derivative molecules, which relatively strengthened the polycationic character of the CS derivatives. Therefore, the antifungal activity of the CS salts was improved.

Image diffusion plays a fundamental role for the task of image denoising. Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a simple but very effective framework for image denoising. However, as the TNRD model is a local model, the diffusion behavior of which is purely controlled by information of local patches, it is prone to create artifacts in the homogenous regions and over-smooth highly textured regions, especially in the case of strong noise levels. Meanwhile, it is widely known that the non-local selfsimilarity (NSS) prior stands as an effective image prior for image denoising, which has been widely exploited in many nonlocal methods. In this work, we are highly motivated to embed the NSS prior into the TNRD model to tackle its weaknesses. In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising. Together with the local filters and influence functions, the non-local filters are learned by employing loss-specific training. The experimental results show that the trained TNLRD model produces visually plausible recovered images with more textures and less artifacts, compared to its local versions. Moreover, the trained TNLRD model can achieve strongly competitive performance to recent state-of-theart image denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).