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Overview of Research on Digital Image Denoising Methods [PDF]

Sensors
During image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like ...
Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu   +3 more
doaj   +2 more sources

Enhanced CNN for image denoising

, 2019
Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train.
Fei, Lunke, Luo, Nan, Tian, Chunwei, Wang, Junqian, Wen, Jie, Xu, Yong   +5 more
core   +2 more sources

Self-Supervised Joint Learning for pCLE Image Denoising [PDF]

Sensors
Probe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles.
Kun Yang, Haojie Zhang, Yufei Qiu, Tong Zhai, Zhiguo Zhang   +4 more
doaj   +2 more sources

Efficient real-world image denoising using multi-scale gaussian pyramids [PDF]

Scientific Reports
The field of image denoising has undergone significant advancements over the years. Recently, Convolutional Neural Networks (CNN) based denoising methods have shown remarkable performance in image denoising.
Asha Rani, Rosepreet Kaur Bhogal
doaj   +2 more sources

Image denoising method integrating ridgelet transform and improved wavelet threshold. [PDF]

PLoS ONE
In the field of image processing, common noise types include Gaussian noise, salt and pepper noise, speckle noise, uniform noise and pulse noise. Different types of noise require different denoising algorithms and techniques to maintain image quality and
Bingbing Li, Yao Cong, Hongwei Mo
doaj   +2 more sources

Terahertz image denoising via multiscale hybrid‐convolution residual network [PDF]

CAAI Transactions on Intelligence Technology
Terahertz imaging technology has great potential applications in areas, such as remote sensing, navigation, security checks, and so on. However, terahertz images usually have the problems of heavy noises and low resolution.
Heng Wu, Zijie Guo, Chunhua He, Shaojuan Luo, Bofang Song   +4 more
doaj   +2 more sources

Overview of Image Denoising Methods

Jisuanji kexue yu tansuo, 2021
In real scenes, due to the imperfections of equipment and systems or the existence of low-light environments, the collected images are noisy. The images will also be affected by additional noise during the compression and transmission process, which will
LIU Liping, QIAO Lele, JIANG Liucheng
doaj   +1 more source

Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior

Applied Sciences, 2022
Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the ...
Shaoping Xu, Xiaojun Chen, Yiling Tang, Shunliang Jiang, Xiaohui Cheng, Nan Xiao   +5 more
doaj   +1 more source

Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm

Technologies, 2023
Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details.
Rusul Sabah Jebur, Mohd Hazli Bin Mohamed Zabil, Dalal Abdulmohsin Hammood, Lim Kok Cheng, Ali Al-Naji   +4 more
doaj   +1 more source

Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning

Applied Sciences, 2023
In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition.
Roopdeep Kaur, Gour Karmakar, Muhammad Imran   +2 more
doaj   +1 more source