Implementing canny edge detection algorithm for noisy image

Sekehravani, Ehsan Akbari; Babulak, Eduard; Masoodi, Mehdi

doi:10.11591/eei.v9i4.1837

Cited by 77 publications

(44 citation statements)

References 19 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Image gradients in GAN-based inpainting Image gradients are often used by different image processing techniques [46,47]. This is due to the fact that the human vision is far more sensitive to gradients than it is to overall pixel intensity [48,49,50]. Because known and unknown areas are representations of the masked image in image inpainting, implementing a gradient method to identify occlusion boundaries might be beneficial.…”

Section: Attention-based Inpainting In Gansmentioning

confidence: 99%

V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network

Jam¹,

Kendrick²,

Drouard³

et al. 2022

Preprint

View full text Add to dashboard Cite

Deep learning methods outperform traditional methods in image inpainting. In order to generate contextual textures, researchers are still working to improve on existing methods and propose models that can extract, propagate, and reconstruct features similar to ground-truth regions. Furthermore, the lack of a high-quality feature transfer mechanism in deeper layers contributes to persistent aberrations on generated inpainted regions. To address these limitations, we propose the V-LinkNet cross-space learning strategy network. To improve learning on contextualised features, we design a loss model that employs both encoders. In addition, we propose a recursive residual transition layer (RSTL).The RSTL extracts high-level semantic information and propagates it down layers. Finally, we compare inpainting performance on the same face with different masks and on different faces with the same masks. To improve image inpainting reproducibility, we propose a standard protocol to overcome biases with various masks and images. We investigate the V-LinkNet components using experimental methods. Our result surpasses the state of the art when evaluated on the CelebA-HQ with the standard protocol. In addition, our model can generalise well when evaluated on Paris Street View, and Places2 datasets with the standard protocol.

show abstract

Section: Attention-based Inpainting In Gansmentioning

confidence: 99%

V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network

Jam¹,

Kendrick²,

Drouard³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Canny filtering is applied to image 4 ( , ) to obtain the circle of the fiber cladding. The Canny filtering involve: smoothed using a Gaussian kernel, obtaining of edge strength from Sobel filtering, calculation and quantization of the edge direction, nonmaximum suppression and thresholding with hysteresis [15]. In this case, two hysteresis thresholds ( = 110 and = 50) are set to obtain the shapes and borders of the objects in the thresholding.…”

Section: Canny Filteringmentioning

confidence: 99%

Device to evaluate cleanliness of fiber optic connectors using image processing and neural networks

Fernández

Chávez

Kemper

2021

IJECE

View full text Add to dashboard Cite

This work proposes a portable, handheld electronic device, which measures the cleanliness in fiber optic connectors via digital image processing and artificial neural networks. Its purpose is to reduce the evaluation subjectivity in visual inspection done by human experts. Although devices with this purpose already exist, they tend to be cost-prohibitive and do not take advantage of neither image processing nor artificial intelligence to improve their results. The device consists of an optical microscope for fiber optic connector analysis, a digital camera adapter, a reduced-board computer, an image processing algorithm, a neural network algorithm and an LCD screen for equipment operation and results visualization. The image processing algorithm applies grayscale histogram equalization, Gaussian filtering, Canny filtering, Hough transform, region of interest segmentation and obtaining radiometric descriptors as inputs to the neural network. Validation consisted of comparing the results by the proposed device with those obtained by agreeing human experts via visual inspection. Results yield an average Cohen's Kappa of 0.926, which implies a very satisfactory performance by the proposed device.

show abstract

“…Xiafeng Li [28] presented a deep learning-based cancer picture edge identification method. For a noisy picture, Akbari Sekehravani [29] developed a canny edge detection method. S. A. M. H. K. Vignesh [30] suggested utilising fractional derivatives and information sets to identify edges.…”

Section: Literature Reviewmentioning

confidence: 99%

“…1. Types of image compression[26] b) Edge detection[28,29,30,31,32]: The process of extracting the outlines or border from image is termed as edge detection. To reduce image content during of feature selection there would be need of edge detection.…”

mentioning

confidence: 99%

A Review on Quality of Image during CBIR Operations and Compression

Singla¹,

Kumar²,

Dhingra³

2021

SCRS Conference Proceedings on Intelligent Systems

View full text Add to dashboard Cite

The research paper is review the quality of image at the time of CBIR operation. Research is opting to maintain the quality of image even after performing compression. Research work has highlighted the impact of CBIR operation and compression operation over quality of image. The major issue in image processing research is impact of image processing over image quality. Moreover there is need to discuss loss less compression mechanism to retain quality of image. Research paper is considering different methodologies used by existing research paper along with their working mechanism, advantages and disadvantage. Research is considering image processing as process of extracting information from pictures and combining it for use in a variety of applications. Image processing programmes are useful in a variety of situations. A few examples include medical imaging, industrial applications. The remote sensing, spaces as well as military applications are also considered. The application of computer vision mechanism to the graphic content retrieval problem has been considered a challenge in finding digital pictures in huge databases. Existing researches have worked to improve the overall performance during image processing by retaining quality of the image. Moreover, there is increase in accurate decisions making. Research is opting to retain the quality of image during CBIR operations.

show abstract

Implementing canny edge detection algorithm for noisy image

Cited by 77 publications

References 19 publications

V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network

V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network

Device to evaluate cleanliness of fiber optic connectors using image processing and neural networks

A Review on Quality of Image during CBIR Operations and Compression

Contact Info

Product

Resources

About