Image quality evaluation of iterative CT reconstruction algorithms: a perspective from spatial domain noise texture measures

2012 ◽  
Author(s):  
Jan H. Pachon ◽  
Girijesh Yadava ◽  
Debashish Pal ◽  
Jiang Hsieh
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Spatial Domain ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Image Quality Evaluation ◽  
Iterative Ct Reconstruction

Image quality evaluation of a small animal PET scanner

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Andréa Vidal Ferreira ◽  
Rodrigo Modesto Gadelha Gontijo ◽  
Guilherme Cavalcante de Albuquerque Souza ◽  
Bruno Melo Mendes ◽  
Juliana Batista da Silva ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Small Animal ◽  
Small Animal Pet ◽  
Image Quality Evaluation ◽  
Animal Pet

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

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