Endoscopic Image Denoising Algorithm Based on Spatial Attention UNet

Zhang, Biao; Li, Mianhao; Zhou, Changjiang; Yang, Qing; Wang, Liqiang; Yuan, Bo

doi:10.1088/1742-6596/2400/1/012026

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three quantitative evaluation indexes are used to quantitatively evaluate the authenticity and accuracy of clothing images generated by PSPNet ( Aoki et al, 2019 ), VGG ( Takagi et al, 2017 ), ResNet ( Takagi et al, 2017 ), UNet ( Zhang et al, 2022 ), Attention-UNet ( Zhu, Shu & Zhang, 2022 ), GAN ( Chen & Han, 2018 ), Transformer ( Liu, Luo & Dong, 2019 ) and this method. (1) Authenticity evaluation: The experiment adopts human perceptual research, (HPR).…”

Section: Experiments and Analysismentioning

confidence: 99%

An improved image processing algorithm for visual characteristics in graphic design

Zhou¹

2023

PeerJ Computer Science

View full text Add to dashboard Cite

Drawing the clothing plan is an essential part of the clothing industry. However, the irregular shape of clothing, strong deformability and sensitivity to light make the fast and accurate realization of clothing image retrieval a very challenging problem. The successful application of the Transformer in image recognition shows the application potential of the Transformer in the image field. This article proposes an efficient and improved clothing plan based on ResNet-50. Firstly, in the feature extraction section, the ResNet-50 network structure embedded in the Transformer module is used to improve the network’s receptive field range and feature extraction ability. Secondly, dense jump connections are added to the ResNet-50 upsampling process, making full use of feature extraction information at each stage, further improving the quality of the generated image. The network consists of three steps: the sketch stage, which aims to predict the color distribution of clothing and obtain watercolor images without gradients and shadows. The second is the thinning stage, which refines the watercolor image into a clothing image with light and shadow effect; The third is the optimization stage, which combines the outputs of the first two stages to optimize the generation quality further. The experimental results show that the improved network’s IS and first input delay (FID) scores are 4.592 and 1.506, respectively. High-quality clothing images can be generated only by inputting line drawings and a few color points. Compared with the existing methods, the image generated by this network has excellent advantages in realism and accuracy. This method can combine various feature information of images, improve retrieval accuracy, has strong robustness and practicability, and can provide a reference for the daily work of fashion designers.

show abstract

Section: Experiments and Analysismentioning

confidence: 99%

An improved image processing algorithm for visual characteristics in graphic design

Zhou¹

2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Filtering or Denoising Algorithm Selection: Once the noise characteristics are understood, the next step is to choose an appropriate denoising algorithm [27,28] or filter. The choice depends on factors such as the type and intensity of noise, the desired level of noise reduction, and the importance of preserving image details.…”

Section: Procedures Of Image Denoisingmentioning

confidence: 99%

Wavelet Analysis for Image Denoising: A Multiscale Approach for Enhancing Visual Clarity

2023

Preprint

View full text Add to dashboard Cite

This paper explores the application of wavelet analysis, a multiscale approach for enhancing visual clarity, in the context of image denoising, which reduces the impact of noisy pixels, caused by various factors such as electronic sensor limitations, low-light conditions, or transmission errors in digital imaging systems. This paper introduces some common noise types like Gaussian noise and processes of image denoising, eliciting the strength of wavelet analysis. As a powerful image denoising technology, wavelet analysis needs five steps to process the image, and the key step is thresholding. There are many kinds of wavelet, and each wavelet has different advantages and functions, which makes it suitable for different applications. Hence, wavelet analysis also makes a contribution in various fields beyond signal processing and data analysis, such as Biomedical Imaging and Geophysics and Seismology, where preserving image quality is essential for accurate analysis and interpretation. In short, this research highlights the promise of wavelet analysis, emphasizing the use of high- quality image data.

show abstract

A remote sensing image de-noising method based on wavelet packet decomposition and information entropy threshold function

Ma,

Zhao,

Zhang

2024

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024)

View full text Add to dashboard Cite

Endoscopic Image Denoising Algorithm Based on Spatial Attention UNet

Cited by 3 publications

References 11 publications

An improved image processing algorithm for visual characteristics in graphic design

An improved image processing algorithm for visual characteristics in graphic design

Wavelet Analysis for Image Denoising: A Multiscale Approach for Enhancing Visual Clarity

A remote sensing image de-noising method based on wavelet packet decomposition and information entropy threshold function

Contact Info

Product

Resources

About