Subjective and objective quality assessment for image restoration: A critical survey

Hu, Bo; Li, Leida; Wu, Jinjian; Qian, Jiansheng

doi:10.1016/j.image.2020.115839

Cited by 26 publications

(7 citation statements)

References 110 publications

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“…The RR method analyzes the image to be evaluated using partial information of the ideal image as a reference. The NR method is based on the statistics of the image, completely free from the dependence on the ideal reference image 33 . In this paper, we focus on the clarity of the fused image, we therefore use the NR method for image quality evaluation.…”

Section: Proposed Methodsmentioning

confidence: 99%

Large depth-of-field image fusion method for complex gyration class mechanical parts

Zhang

Guo

et al. 2023

J. Electron. Imag.

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.The local regions of complex gyration class mechanical parts are largely different, which results in the problem that their surface images are partially clear. This problem directly affects the effectiveness of vision methods on detecting surface defects. To tackle this problem, a large depth-of-field (DoF) surface image fusion method is proposed to obtain a full-focus image of complex mechanical parts, such as gyration class mechanical parts. We design an image acquisition platform based on the characteristics of gyration class parts to acquire their surface images accurately and clearly. We then propose a feature detection-based image registration method, by which the registered image can represent the surface information of the part completely and accurately. Additionally, we adopt a convolutional neural network-based image fusion method to achieve a fused large DoF surface image. Experimental studies were conducted to evaluate the performance of the deep-learning-based methods. The experimental results show that the proposed method can completely and clearly fuse the large DoF surface images of complex gyration class mechanical parts. The quality of the fused images has a significant improvement, and the proposed method is significantly more efficient than traditional fusion methods and other deep-learning methods.

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Section: Proposed Methodsmentioning

confidence: 99%

Large depth-of-field image fusion method for complex gyration class mechanical parts

Zhang

Guo

et al. 2023

J. Electron. Imag.

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“…Following this, the difference between the original image and ESRGAN generated images were compared based on several metrics which include mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), universal image quality index (UQI), multi-scale structural similarity index (MSSSIM), erreur relative globale adimensionnelle de synthese (ERGAS), spatial correlation coefficient (SCC), relative average spectral error (RASE) and spectral angle mapper (SAM). [90,91]…”

Section: Performance Comparisonmentioning

confidence: 99%

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

Adhikary¹

2022

Preprint

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A smart detection and recognition model for the species of a fish from camera footage is of urgent requirement as fishery contributes to a large portion of the world economy and these kinds of advanced models can aid fishermen on a large scale. Such models incorporating a pick-and-place machine can be very useful to sort different fish species in a bulk without human intervention and this can greatly reduce costs for large scale fishing industries. Existing methods for detecting and recognizing fish species have many limitations such as limited scalability, detection accuracy, failing to detect multiple species, degraded performance at a lower resolution, or pinpointing the exact location of the fish. By modifying the head of a very powerful deep learning model namely VGG-16 with pre-trained weights can be used to detect both the species of the fish and find the exact location of the fish in an image by implementing a modified YOLO to incorporate the bounding box regression head. We have proposed the usage of the ESRGAN algorithm along with the proposed neural network to amplify the image resolution by a factor of 4. With this method, the overall detection accuracy of 96.5% has been obtained. The experiment has been conducted based on a total of 9460 images spread across 9 species. After further improving the model, a pick-and-place machine could be integrated for very fast sorting of the fish according to their species in different large-scale fish industries.

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“…For the sake of completeness, we also want to note that several metrics exist that target particularly degraded images (like reconstructions with few projections as used in this work) and relate them to the performance of image restoration methods [103]. While these quality indicators are mostly unsuited for investigations like the one conducted here, in the future some of them might be interesting in modified versions for the assessment of artefact reduction algorithms or image enhancement methods.…”

Section: Image Quality Metrics For Reconstruction Evaluationmentioning

confidence: 99%

Selection and evaluation of spherical acquisition trajectories for industrial computed tomography

2021

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In conventional industrial computed tomography, the source–detector system rotates in equiangular steps in-plane relative to the part of investigation. While being by far the most frequently used acquisition trajectory today, this method has several drawbacks like the formation of cone beam artefacts or limited usability in case of geometrical restrictions. In such cases, the usage of alternative spherical trajectories can be beneficial to improve image quality and defect visibility. While investigations have been performed to relate the influence of the trajectory choice in the typical metrological case of a high number of available projections, so far barely any work has been done for the case of few source–detector poses, which is more relevant in the field of non-destructive testing. In this work, we provide an overview of quantitative metrics that can be used to assess the image quality of reconstructed computed tomography volumes, discuss their advantages and drawbacks and propose a framework to investigate the performance of several non-standard trajectories with respect to previously defined regions of interest. Inspired by pseudorandom sampling methods for Monte–Carlo-algorithms, we also suggest an entirely new trajectory design, the low-discrepancy spherical trajectory, which extends the concept of equiangular planar trajectories into three dimensions and can be used for benchmarking and comparison with other spherical trajectories. Last, we use an optimization method to calculate task-specific acquisition trajectories and relate their performance to other spherical designs.

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Subjective and objective quality assessment for image restoration: A critical survey

Cited by 26 publications

References 110 publications

Large depth-of-field image fusion method for complex gyration class mechanical parts

Large depth-of-field image fusion method for complex gyration class mechanical parts

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

Selection and evaluation of spherical acquisition trajectories for industrial computed tomography

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