Multiscale reconstruction of porous media based on multiple dictionaries learning

Digital modeling of the microstructure is important for studying the physical and transport properties of porous media. Multiscale modeling for porous media can accurately characterize macro-pores and micro-pores in a large-FoV (field of view) high-resolution three-dimensional pore structure model. This paper proposes a multiscale reconstruction algorithm based on multiple dictionaries learning, in which edge patterns and micro-pore patterns from homology high-resolution pore structure are introduced into low-resolution pore structure to build a fine multiscale pore structure model. The qualitative and quantitative comparisons of the experimental results show that the results of multiscale reconstruction are similar to the real high-resolution pore structure in terms of complex pore geometry and pore surface morphology. The geometric, topological and permeability properties of multiscale reconstruction results are almost identical to those of the real high-resolution pore structures. The experiments also demonstrate the proposal algorithm is capable of multiscale reconstruction without regard to the size of the input. This work provides an effective method for fine multiscale modeling of porous media

Activating More Information in Arbitrary-Scale Image Super-Resolution

Single-image super-resolution (SISR) has experienced vigorous growth with the rapid development of deep learning. However, handling arbitrary scales (e.g., integers, nonintegers, or asymmetric) using a single model remains a challenging task. Existing super-resolution (SR) networks commonly employ static convolutions during feature extraction, which cannoteffectively perceive changes in scales. Moreover, these continuous scale upsampling modules only utilize the scale factors, without considering the diversity of local features. To activate more information for better reconstruction, two plug-in and compatible modules for fixed-scale networks are designed to perform arbitrary-scale SR tasks. Firstly, we design a Scale-aware Local Feature Adaptation Module (SLFAM), which adaptively adjusts the attention weights of dynamic filters based on the local features and scales. It enables the network to possess stronger representation capabilities. Then we propose a Local Feature AdaptationUpsampling Module (LFAUM), which combines scales and local features to perform arbitrary-scale reconstruction. It allows the upsampling to adapt to local structures. Besides, deformable convolution is utilized letting more information to be activated in the reconstruction, enabling the network to better adapt to the texture features. Extensive experiments on various benchmark datasets demonstrate that integrating the proposed modules into a fixed-scale SR network enables it to achieve satisfactory results with non-integer or asymmetric scales while maintaining advanced performance with integer scales