LAUSR

The widespread use of scanning electron microscopy (SEM) has increased the requirements for SEM image quality. SEM images obtained by electron beam feedback have more complex texture features than natural images obtained by optical imaging, and this condition results in poor performance of algorithms used for assessing natural image quality on SEM datasets,meanwhile,the field of SEM image quality assessment(IQA) is mostly aimed at specific distortion types. In order to solve the above two problems,to address the rich texture, few edges, and extreme sensitivity to the distortion degree of SEM images, we propose a texture and semantic IQA (TSIQA) method for SEM images based on sparse mask and information entropy increase. First, we construct a neural network containing sparse mask module (SMM), which is used to extract intuitive texture features in the spatial and channel domains. Simultaneously, information growth attention (IGA) is introduced into SMM to detect the difference between current and past features of the network for extracting deep semantic information. The quality assessment experiments on SEM image datasets show that compared with the state-of-the-art IQA methods, including popular no-reference techniques adapted to the SEM-IQA, the TSIQA has superiority in typical criteria.