Normalized Weighting Schemes for Image Interpolation Algorithms

Rukundo, Olivier

doi:10.3390/app13031741

Cited by 5 publications

(5 citation statements)

References 49 publications

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“…Interpolation is a technique that pervades or penetrates many applications [34,[38][39][40][41][42]. Interpolation is rarely the goal (in itself), yet it affects both the desired results and the ways to obtain them [16].…”

Section: Selected Methods For Image Interpolationmentioning

confidence: 99%

“…Therefore, in this paper, the author studied such effects using different interpolation algorithms. To the best of the author's knowledge, image interpolation algorithms are divided into two major categories of non-extra-pixel and extra-pixel interpolation algorithms [34]. Unlike the extra-pixel approach, the non-extrapixel approach only uses original or source image pixels to produce or output interpolated images of the desired size [35].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Image Size on Deep Learning

Rukundo

2023

Electronics

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In this work, the best size for late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) images in the training dataset was determined to optimize deep learning training outcomes. Non-extra pixel and extra pixel interpolation algorithms were used to determine the new size of the LGE-MRI images. A novel strategy was introduced to handle interpolation masks and remove extra class labels in interpolated ground truth (GT) segmentation masks. The expectation maximization, weighted intensity, a priori information (EWA) algorithm was used for the quantification of myocardial infarction (MI) in automatically segmented LGE-MRI images. Arbitrary threshold, comparison of the sums, and sums of differences are methods used to estimate the relationship between semi-automatic or manual and fully automated quantification of myocardial infarction (MI) results. The relationship between semi-automatic and fully automated quantification of MI results was found to be closer in the case of bigger LGE MRI images (55.5% closer to manual results) than in the case of smaller LGE MRI images (22.2% closer to manual results).

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Section: Selected Methods For Image Interpolationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of Image Size on Deep Learning

Rukundo

2023

Electronics

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“…The performance of the proposed method is qualitatively evaluated by observing the differences in features between the images after artifact correction and the reference (label) images. For quantitative evaluation, there are full-reference and no-reference metrics [33]. Given the availability of label images, we utilize full-reference metrics (PSNR and MS-SSIM [34]) to evaluate the performance of the proposed artifact correction method.The larger the peak signal-to-noise ratio, the better the quality of the network output image.…”

Section: Data Preparation and Image Quality Assessmentmentioning

confidence: 99%

Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT

Huang,

Li,

Sun

et al. 2024

Applied Sciences

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High-pixel-number synchrotron radiation computed tomography (CT) has the advantages of high sensitivity, high resolution, and a large field of view. It has been widely used in biomedicine, cultural heritage research, non-destructive testing, and other fields. The Nyquist sampling theorem states that when the detector’s pixels per row are increased, it requires more CT projections, resulting in a lengthened CT scan time and increased radiation damage. Sparse-view CT can significantly reduce radiation damage and improve the projection data acquisition speed. However, there is insufficient sparse projection data, and the slices reconstructed show aliasing artifacts. Currently, aliasing artifact correction processes more medical CT images, and the number of pixels of such images is small (mainly 512×512 pixels). This paper presents an aliasing artifact correction algorithm based on deep learning for synchrotron radiation CT with a high pixel number (1728×1728 pixels). This method crops high-pixel-number CT images with aliasing artifacts into patches with overlapping features. During the network training process, a convolutional neural network is utilized to enhance the details of the patches, after which the patches are reintegrated into a new CT slice. Subsequently, the network parameters are updated to optimize the new CT slice that closely approximates the full-view slice. To align with practical application requirements, the neural network is trained using only three samples to optimize network parameters and applied successfully to untrained samples for aliasing artifact correction. Comparative analysis with typical deep learning aliasing artifact correction algorithms demonstrates the superior ability of our method to correct aliasing artifacts while preserving image details more effectively. Furthermore, the effect of aliasing artifact correction at varying levels of projection sparsity is investigated, revealing a positive correlation between image quality after deep learning processing and the number of projections. However, the trade-off between rapid experimentation and artifact correction remains a critical consideration.

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“…In this paper, we have selected bilinear interpolation as the method for up-sampling high-dimensional features based on several experimental results. The experimental results comparing bilinear interpolation and deconvolution up-sampling indicate that bilinear interpolation outperforms deconvolution in terms of both parameter count and resulting image quality [45]. The poor performance of deconvolution could be attributed to the limited availability of training samples.…”

Section: Feature Fusionmentioning

confidence: 99%

“…We performed ablation experiments on the SIRST_AUG dataset [45] to assess the efficacy of our proposed module and evaluate its impact on the overall performance of the network.…”

Section: Ablation Experimentsmentioning

confidence: 99%

Residual Depth Feature-Extraction Network for Infrared Small-Target Detection

Lizhe

Zhang

et al. 2023

Electronics

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Deep-learning methods have exhibited exceptional performance in numerous target-detection domains, and their application is steadily expanding to include infrared small-target detection as well. However, the effect of existing deep-learning methods is weakened due to the lack of texture information and the low signal-to-noise ratio of infrared small-target images. To detect small targets in infrared images with limited information, a depth feature-extraction network based on a residual module is proposed in this paper. First, a global attention guidance enhancement module (GAGEM) is used to enhance the original infrared small target image in a single frame, which considers the global and local features. Second, this paper proposes a depth feature-extraction module (DFEM) for depth feature extraction. Our IRST-Involution adds the attention mechanism to the classic Involution module and combines it with the residual module for the feature extraction of the backbone network. Finally, the feature pyramid with self-learning weight parameters is used for feature fusion. The comparative experiments on three public datasets demonstrate that our proposed infrared small-target detection algorithm exhibits higher detection accuracy and better robustness.

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Normalized Weighting Schemes for Image Interpolation Algorithms

Cited by 5 publications

References 49 publications

Effects of Image Size on Deep Learning

Effects of Image Size on Deep Learning

Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT

Residual Depth Feature-Extraction Network for Infrared Small-Target Detection

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