Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks

Numerous image superresolution (SR) algorithms have been proposed for reconstructing high-resolution (HR) images from input images with lower spatial resolutions. However, effectively evaluating the perceptual quality of SR images remains a challenging research problem. In this paper, we propose a no-reference/blind deep neural network-based SR image quality assessor (DeepSRQ). To learn more discriminative feature representations of various distorted SR images, the proposed DeepSRQ is a two-stream convolutional network including two subcomponents for distorted structure and texture SR images. Different from traditional image distortions, the artifacts of SR images cause both image structure and texture quality degradation. Therefore, we choose the two-stream scheme that captures different properties of SR inputs instead of directly learning features from one image stream. Considering the human visual system (HVS) characteristics, the structure stream focuses on extracting features in structural degradations, while the texture stream focuses on the change in textural distributions. In addition, to augment the training data and ensure the category balance, we propose a stride-based adaptive cropping approach for further improvement. Experimental results on three publicly available SR image quality databases demonstrate the effectiveness and generalization ability of our proposed DeepSRQ method compared with state-of-the-art image quality assessment algorithms

Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy

Abstract Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans

Ion exchange resins catalysed esterification for the production of value added petrochemicals and oleochemicals

This book chapter is formulated with the aim to review the literature relevant to the esterification reactions catalysed by strong acidic ion exchange resins. Priority has been given to the works that have been published during the last 15 years. Industrially important esterification reactions in the petrochemical and oleochemical industries have been delineated. Various types of strongly acidic ion exchange resins produced by different manufacturers have been used to accelerate the rate of esterification reactions. In the esterification reactions in both petrochemical and oleochemical industries, gel-type resins showed comparable activity with the macro-reticular type resins after swollen by the polar solvents during the reactions, otherwise the macroporous-type resins always performed better. Gel-type resins were also preferred, in particular, for the esterification reactions involved bulky reactant molecules due to the mass transfer restriction of the macro-reticular type resins. In contrast to the comprehensive studies on the activity of ion exchange resin in the esterification reactions, the works that are related to the reusability, recovery and regeneration of these resin catalysts are rather scarce. In order to scale up the esterification processes catalysed by strongly acidic ion exchange resins to the industrial level, future works should be focusing on the solutions to overcome of the aforementioned constraints and limitations