Motion deblurring using edge map with blurred/noisy image pairs

2012 ◽  
Vol 285 (7) ◽  
pp. 1777-1786 ◽  
Author(s):  
Ser-Hoon Lee ◽  
Hyung-Min Park ◽  
Sun-Young Hwang
Keyword(s):  
Noisy Image ◽  
Motion Deblurring ◽  
Image Pairs

Blurred and noisy image pairs in parallel optics

2014 ◽  
Vol 31 (11) ◽  
pp. 2529 ◽  
Author(s):  
Iftach Klapp ◽  
Nir Sochen ◽  
David Mendlovic
Keyword(s):  
Noisy Image ◽  
Image Pairs

scholarly journals Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (21) ◽  
pp. 4383
Author(s):  
Gang Zhang ◽  
Zhi Li ◽  
Xuewei Li ◽  
Sitong Liu
Keyword(s):  
Synthetic Aperture Radar ◽  
Real World ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Noisy Image ◽  
Deep Convolutional Neural Networks ◽  
Sar Images ◽  
Radar Images ◽  
Image Pairs ◽  
Aperture Radar

Self-supervised method has proven to be a suitable approach for despeckling on synthetic aperture radar (SAR) images. However, most self-supervised despeckling methods are trained by noisy-noisy image pairs, which are constructed by using natural images with simulated speckle noise, time-series real-world SAR images or generative adversarial network, limiting the practicability of these methods in real-world SAR images. Therefore, in this paper, a novel self-supervised despeckling algorithm with an enhanced U-Net is proposed for real-world SAR images. Firstly, unlike previous self-supervised despeckling works, the noisy-noisy image pairs are generated from real-word SAR images through a novel generation training pairs module, which makes it possible to train deep convolutional neural networks using real-world SAR images. Secondly, an enhanced U-Net is designed to improve the feature extraction and fusion capabilities of the network. Thirdly, a self-supervised training loss function with a regularization loss is proposed to address the difference of target pixel values between neighbors on the original SAR images. Finally, visual and quantitative experiments on simulated and real-world SAR images show that the proposed algorithm notably removes speckle noise with better preserving features, which exceed several state-of-the-art despeckling methods.

scholarly journals Noise2Grad: Extract Image Noise to Denoise

2021 ◽  
Author(s):  
Huangxing Lin ◽  
Yihong Zhuang ◽  
Yue Huang ◽  
Xinghao Ding ◽  
Xiaoqing Liu ◽  
...  
Keyword(s):  
Image Denoising ◽  
Noise Removal ◽  
Background Information ◽  
Noisy Image ◽  
Paired Data ◽  
Noisy Input ◽  
Data Pair ◽  
Image Pairs ◽  
Noise Statistics ◽  
Noise Map

In many image denoising tasks, the difficulty of collecting noisy/clean image pairs limits the application of supervised CNNs. We consider such a case in which paired data and noise statistics are not accessible, but unpaired noisy and clean images are easy to collect. To form the necessary supervision, our strategy is to extract the noise from the noisy image to synthesize new data. To ease the interference of the image background, we use a noise removal module to aid noise extraction. The noise removal module first roughly removes noise from the noisy image, which is equivalent to excluding much background information. A noise approximation module can therefore easily extract a new noise map from the removed noise to match the gradient of the noisy input. This noise map is added to a random clean image to synthesize a new data pair, which is then fed back to the noise removal module to correct the noise removal process. These two modules cooperate to extract noise finely. After convergence, the noise removal module can remove noise without damaging other background details, so we use it as our final denoising network. Experiments show that the denoising performance of the proposed method is competitive with other supervised CNNs.

Image deblurring with blurred/noisy image pairs

2007 ◽  
Vol 26 (3) ◽  
pp. 1 ◽  
Author(s):  
Lu Yuan ◽  
Jian Sun ◽  
Long Quan ◽  
Heung-Yeung Shum
Keyword(s):  
Image Deblurring ◽  
Noisy Image ◽  
Image Pairs
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