Superpixel-based Structural Similarity Metric for Image Fusion Quality Evaluation

Wang, Eryan; Yang, Bin; Pang, Lihui

doi:10.1007/s11220-021-00339-1

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…More recent task-specific studies explored IQA for the images of scanned documents [14] and screen content [15]. Likewise, fused images [16], smartphone photographs [17], remote sensing data [18], and climate patterns [19] demanded the development of targeted IQA approaches. Historically, many of these works have been focusing on the quality degradation caused by the compression algorithms [19]- [22], with relatively small datasets appearing publicly for the IQ evaluation.…”

Section: Related Workmentioning

confidence: 99%

Image Quality Assessment for Magnetic Resonance Imaging

et al. 2023

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Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the image quality. The growing popularity of image enhancement, generation, and recovery models instigated the development of many methods to assess their performance. However, most IQA solutions are designed to predict image quality in the general domain, with the applicability to specific areas, such as medical imaging, remaining questionable. Moreover, the selection of these IQA metrics for a specific task typically involves intentionally induced distortions, such as manually added noise or artificial blurring; yet, the chosen metrics are then used to judge the output of real-life computer vision models. In this work, we aspire to fill these gaps by carrying out the most extensive IQA evaluation study for Magnetic Resonance Imaging (MRI) to date (14,700 subjective scores). We use outputs of neural network models trained to solve problems relevant to MRI, including image reconstruction in the scan acceleration, motion correction, and denoising. Our emphasis is on reflecting the radiologist's perception of the reconstructed images, gauging the most diagnostically influential criteria for the quality of MRI scans: signal-to-noise ratio, contrast-to-noise ratio, and the presence of artifacts. Seven trained radiologists assess these distorted images, with their verdicts then correlated with 35 different image quality metrics (full-reference, no-reference, and distribution-based metrics considered). The top performers -DISTS, HaarPSI, VSI, and FID VGG16 -are found to be efficient across three proposed quality criteria, for all considered anatomies and the target tasks.

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Section: Related Workmentioning

confidence: 99%

Image Quality Assessment for Magnetic Resonance Imaging

et al. 2023

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“…Shang et al designed and developed a software system for sandstone microscopic image analysis [ 14 ]. Wang et al believe that the system first uses SLIC superpixel algorithm to divide the image into hundreds of superpixels with regular shape and then measures the similarity between adjacent super pixels and combines the oversegmented regions so that the final generated superpixels contain complete mineral particle regions [ 15 ]. In the follow-up work of their team, they proposed a SLIC algorithm based on multiangle images; that is, using the sandstone image taken under the multiangle polarizing microscope, first generate the superpixels with boundary adhesion and oversegmentation, then extract the boundary, color, and texture features of each super pixel, and then merge the super pixels twice, and finally segment the more obvious feldspar, quartz, and other particles in the sandstone.…”

Section: Literature Reviewmentioning

confidence: 99%

Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation

Liu

Jin

2022

International Journal of Analytical Chemistry

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In order to study the needs of identifying rock thin-section samples by manual observation in the field of geology, a method of electrochemical intelligent recognition of mineral materials based on superpixel image segmentation is proposed. The image histogram of this method can be used to represent the distribution of each pixel value of the image. This interval is consistent with the number of pixels in the method. And using the experiment, the CPU used in the experiment is Intel® Core™ i7-8700 3.2 GHz, the memory is 16 GB, and the GPU is NVIDIA GeForce GT × 1080 Ti, which ensures the accuracy of the experiment. Based on all the experimental results, it can be seen that after the two-stage processing of the designed superpixel algorithm and the region merging algorithm, the final sandstone slice image segmentation results are close to the results of manual labeling, which is helpful for the subsequent research on sandstone component identification. The feasibility of this method was verified.

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“…More recent task-specific studies explored IQA for the images of scanned documents [37] and screen content [72]. Likewise, fused images [65], smartphone photographs [27], remote sensing data [31], and climate patterns [6] demanded the development of targeted IQA approaches. Historically, many of these works have been focusing on the quality degradation caused by the compression algorithms [2], [5], [6], [63], with relatively small datasets appearing publicly for the IQ evaluation.…”

Section: Related Workmentioning

confidence: 99%

Image Quality Assessment for Magnetic Resonance Imaging

Kastryulin¹,

Zakirov²,

Pezzotti³

et al. 2022

Preprint

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Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the image quality. The growing popularity of image enhancement, generation, and recovery models instigated the development of many methods to assess their performance. However, most IQA solutions are designed to predict image quality in the general domain, with the applicability to specific areas, such as medical imaging, remaining questionable. Moreover, the selection of these IQA metrics for a specific task typically involves intentionally induced distortions, such as manually added noise or artificial blurring; yet, the chosen metrics are then used to judge the output of real-life computer vision models. In this work, we aspire to fill these gaps by carrying out the most extensive IQA evaluation study for Magnetic Resonance Imaging (MRI) to date (14,700 subjective scores). We use outputs of neural network models trained to solve problems relevant to MRI, including image reconstruction in the scan acceleration, motion correction, and denoising. Seven trained radiologists assess these distorted images, with their verdicts then correlated with 35 different image quality metrics (full-reference, no-reference, and distribution-based metrics considered). Our emphasis is on reflecting the radiologist's perception of the reconstructed images, gauging the most diagnostically influential criteria for the quality of MRI scans: signal-to-noise ratio, contrast-to-noise ratio, and the presence of artifacts.

show abstract

Superpixel-based Structural Similarity Metric for Image Fusion Quality Evaluation

Cited by 4 publications

References 42 publications

Image Quality Assessment for Magnetic Resonance Imaging

Image Quality Assessment for Magnetic Resonance Imaging

Electrochemical Intelligent Recognition of Mineral Materials Based on Superpixel Image Segmentation

Image Quality Assessment for Magnetic Resonance Imaging

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