Vignetting Correction Based on a Two-Dimensional Gaussian Filter With Harmony for Area Array Sensors

Cao, Hongtao; Gu, Xingfa; Zhang, Mixia; Zhang, Haifeng; Chen, Xinran

doi:10.1109/tci.2022.3188413

Cited by 8 publications

(3 citation statements)

References 33 publications

(28 reference statements)

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“…Subsequently, the proposed method and the FMGFI are compared. Figures 12,13,14,15 and 16 show the original image, the noise-added image, and the image after applying each filter to the noise-added image for 12 types of sample image. Tables 5 and 6 show the RMSE and SSIM values, respectively, of each sample image filtered using each filter.…”

Section: Methodsmentioning

confidence: 99%

“…High performance and large image sensors can reduce the rate of noise in an image; however, the rate cannot be reduced to zero. Therefore, noise reduction is necessary even for images captured by cameras with large image sensors and digital filtering is essential for digital imaging, image recognition, and super-resolution technology [5][6][7] Noise is generally blurred and reduced using low-pass filters, such as the Gaussian filter (GF) [8][9][10][11][12][13][14] . Such filters have an advantage of reducing noise, however they also have a disadvantage of invariably blurring edge 2,10 .…”

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confidence: 99%

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Edge-preserving smoothing filter using fast M-estimation method with an automatic determination algorithm for basic width

Yamaguchi

Yoshida

Kondo

et al. 2023

Sci Rep

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Digital filtering is essential for digital imaging, image recognition, and super-resolution technology. For example, the presence of noise in images captured by digital cameras causes deterioration of the image quality and image recognition rate. In order to improve the image recognition rate, noise reduction and edge preservation must be performed during preprocessing. Noise is generally reduced using low-pass filters, such as the Gaussian filter. Although they reduce noise, such filters also have the properties of blurring edge. A strong edge blur reduces the accuracy of the feature detection in image recognition. Therefore, in our previous study, a fast M-estimation Gaussian filter for images (FMGFI) was proposed as an image filter that simultaneously achieves denoising and edge preservation. In the FMGFI, the setting of the optimal basic width of the 2nd order B-spline basis functions is important for achieving simultaneous denoising and edge preservation. In this method, the optimal basic width of the FMGFI was determined not only by manually setting the basic width but also by human judgment of the filtered images. Consequently, the inability to automatically determine the optimal basic width hindered efficient denoising during image processing Therefore, in this research, we develop and propose a method that can automatically determine the optimal basic width of the FMGFI. The previously proposed method calculates using the same basic width for all the pixels over the entire image; in contrast, the proposed method calculates using the basic width automatically determined for each pixel. The experiments confirmed that the method proposed in this study achieves higher denoising and edge preservation performance than the ones used in previous research. The results also showed that it has the highest denoising performance against salt-and-pepper noise as compared to other filters: non-local mean filter, Gaussian filter, median filter, bilateral filter, adaptive bilateral filter, and FMGFI. The experimental results for the Gaussian noise sowed that the proposed method has the same denoising and edge preservation performance as the other filters in visual evaluation. From the above, the proposed method is expected to contribute to efficient denoising and improvement of image quality by using it as a preprocessing.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Edge-preserving smoothing filter using fast M-estimation method with an automatic determination algorithm for basic width

Yamaguchi

Yoshida

Kondo

et al. 2023

Sci Rep

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show abstract

“…Light-denoising Total Variation (TV) reduces noise in digital images while preserving details, especially in low-light conditions. By promoting sparsity in gradients in images, total variation regularization effectively restores images corrupted by noise [40].…”

Section: Light Total Variation (Tv)mentioning

confidence: 99%

From Augmentation to Inpainting: Improving Visual SLAM With Signal Enhancement Techniques and GAN-Based Image Inpainting

Theodorou,

Velisavljevic,

Dyo

et al. 2024

IEEE Access

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This paper undertakes a comprehensive investigation that surpasses the conventional examination of signal enhancement techniques and their effects on visual Simultaneous Localization and Mapping (vSLAM) performance across diverse scenarios. Going beyond the conventional scope, the study extends its focus towards the seamless integration of signal enhancement techniques, aiming to achieve a substantial enhancement in the overall vSLAM performance. The research not only delves into the assessment of existing methods but also actively contributes to the field by proposing innovative denoising techniques that can play a pivotal role in refining the accuracy and reliability of vSLAM systems. This multifaceted approach encompasses a thorough exploration of the intricate relationships between signal enhancement, denoising strategies, their cumulative impact on the performance of vSLAM in real-world applications and the innovative use of Generative Adversarial Networks (GANs) for image inpainting. The GANs effectively fill in missing spaces following object detection and removal, presenting a novel state-of-theart approach that significantly enhances overall accuracy and execution speed of vSLAM. This paper aims to contribute to the advancement of vSLAM algorithms in real-world scenarios, demonstrating improved accuracy, robustness, and computational efficiency through the amalgamation of signal enhancement and advanced denoising techniques.INDEX TERMS signal enhancement, denoising techniques, visual SLAM, object detection, simultaneous localization and mapping(SLAM), object detection and generative adversarial network (GAN).

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Design of an Efficient Satellite Image Analysis Model for Identification of Sustainable Construction Areas via Ground Subsidence Monitoring and Infrastructure Monitoring Operations

Chatrabhuj,

Meshram

2024

Indian Geotech J

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Vignetting Correction Based on a Two-Dimensional Gaussian Filter With Harmony for Area Array Sensors

Cited by 8 publications

References 33 publications

Edge-preserving smoothing filter using fast M-estimation method with an automatic determination algorithm for basic width

Edge-preserving smoothing filter using fast M-estimation method with an automatic determination algorithm for basic width

From Augmentation to Inpainting: Improving Visual SLAM With Signal Enhancement Techniques and GAN-Based Image Inpainting

Design of an Efficient Satellite Image Analysis Model for Identification of Sustainable Construction Areas via Ground Subsidence Monitoring and Infrastructure Monitoring Operations

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