Designing A Composite Dictionary Adaptively From Joint Examples

We study the complementary behaviors of external and internal examples in image restoration, and are motivated to formulate a composite dictionary design framework. The composite dictionary consists of the global part learned from external examples, and the sample-specific part learned from internal examples. The dictionary atoms in both parts are further adaptively weighted to emphasize their model statistics. Experiments demonstrate that the joint utilization of external and internal examples leads to substantial improvements, with successful applications in image denoising and super resolution

Deep Networks for Image Super-Resolution with Sparse Prior

Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration problems. For image super-resolution, several models based on deep neural networks have been recently proposed and attained superior performance that overshadows all previous handcrafted models. The question then arises whether large-capacity and data-driven models have become the dominant solution to the ill-posed super-resolution problem. In this paper, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end. The interpretation of the network based on sparse coding leads to much more efficient and effective training, as well as a reduced model size. Our model is evaluated on a wide range of images, and shows clear advantage over existing state-of-the-art methods in terms of both restoration accuracy and human subjective quality

Self-Tuned Deep Super Resolution

Deep learning has been successfully applied to image super resolution (SR). In this paper, we propose a deep joint super resolution (DJSR) model to exploit both external and self similarities for SR. A Stacked Denoising Convolutional Auto Encoder (SDCAE) is first pre-trained on external examples with proper data augmentations. It is then fine-tuned with multi-scale self examples from each input, where the reliability of self examples is explicitly taken into account. We also enhance the model performance by sub-model training and selection. The DJSR model is extensively evaluated and compared with state-of-the-arts, and show noticeable performance improvements both quantitatively and perceptually on a wide range of images

Current advances in capillarity: Theories and applications

As common physical phenomena in porous media, capillarity behaviors exist in many engineering applications and natural science fields. The experimental, theoretical and numerical research on capillarity in porous media has lasted for more than a century, and the research results have been widely used in various fields, such as the development of conventional and unconventional resources. However, although the research has made great progress, the complex imbibition mechanism poses new challenges to us. The 1st National Conference on Imbibition Theory and Application in Porous Media was held in Beijing from April 22 to 24, 2023, to gather  researchers who are interested in imbibition research, exchange the latest progress and achievements in the field of imbibition in porous media, and discuss research hotspots and difficulties.Cited as: Cai, J., Sun, S., Wang, H. Current advances in capillarity: Theories and applications. Capillarity, 2023, 7(2): 25-31. https://doi.org/10.46690/capi.2023.05.0