Establishing an accurate objective evaluation metric of image sharpness is crucial for image analysis, recognition and quality measurement. In this review, we highlight recent advances in no-reference image quality assessment research, divide the reported algorithms into four groups (spatial domain-based methods, spectral domain-based methods, learning-based methods and combination methods) and outline the advantages and disadvantages of each method group. Furthermore, we conduct a brief bibliometric study with which to provide an overview of the current trends from 2013 to 2021 and compare the performance of representative algorithms on public datasets. Finally, we describe the shortcomings and future challenges in the current studies.
As a type of fiber system, nonwoven fabric is ideal for solid–liquid separation and air filtration. With the wide application of nonwoven filter materials, it is crucial to explore the complex relationship between its meso structure and filtration performance. In this paper, we proposed a novel method for constructing the real meso-structure of spun-bonded nonwoven fabric using computer image processing technology based on the idea of a “point-line-body”. Furthermore, the finite element method was adopted to predict filtration efficiencies based on the built 3D model. To verify the effectiveness of the constructed meso-structure and simulation model, filtration experiments were carried out on the fabric samples under different pollution particle sizes and inlet velocities. The experimental results show that the trends observed in the simulation results are consistent with those of the experimental results, with a relative error smaller than 10% for any individual datum.
As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the methods based on boundary segmentation was put forward as a group of image fusion method. Thus, a novel classification method of image fusion algorithms is proposed: transform domain methods, boundary segmentation methods, deep learning methods, and combination fusion methods. In addition, the subjective and objective evaluation standards are listed, and eight common objective evaluation indicators are described in detail. On the basis of lots of literature, this paper compares and summarizes various representative methods. At the end of this paper, some main limitations in current research are discussed, and the future development of multi-focus image fusion is prospected.
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