scholarly journals Blind Poissonian Image Deblurring Regularized by a Denoiser Constraint and Deep Image Prior

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yayuan Feng ◽  
Yu Shi ◽  
Dianjun Sun
Keyword(s):  
Image Deblurring ◽  
Function Estimation ◽  
Split Bregman ◽  
Clear Image ◽  
Split Bregman Iteration ◽  
Noise Filter ◽  
Image Prior ◽  
Degraded Image ◽  
Deep Image ◽  
Blurred Images

The denoising and deblurring of Poisson images are opposite inverse problems. Single image deblurring methods are sensitive to image noise. A single noise filter can effectively remove noise in advance, but it also damages blurred information. To simultaneously solve the denoising and deblurring of Poissonian images better, we learn the implicit deep image prior from a single degraded image and use the denoiser as a regularization term to constrain the latent clear image. Combined with the explicit L0 regularization prior of the image, the denoising and deblurring model of the Poisson image is established. Then, the split Bregman iteration strategy is used to optimize the point spread function estimation and latent clear image estimation. The experimental results demonstrate that the proposed method achieves good restoration results on a series of simulated and real blurred images with Poisson noise.

scholarly journals Dual Image Deblurring Using Deep Image Prior

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2045
Author(s):  
Chang Jong Shin ◽  
Tae Bok Lee ◽  
Yong Seok Heo
Keyword(s):  
Image Deblurring ◽  
Superior Performance ◽  
Natural Image ◽  
Blind Image Deblurring ◽  
Image Prior ◽  
Ill Posed ◽  
Deep Image ◽  
Sharp Image ◽  
Blurred Images ◽  
Blind Image

Blind image deblurring, one of the main problems in image restoration, is a challenging, ill-posed problem. Hence, it is important to design a prior to solve it. Recently, deep image prior (DIP) has shown that convolutional neural networks (CNNs) can be a powerful prior for a single natural image. Previous DIP-based deblurring methods exploited CNNs as a prior when solving the blind deburring problem and performed remarkably well. However, these methods do not completely utilize the given multiple blurry images, and have limitations of performance for severely blurred images. This is because their architectures are strictly designed to utilize a single image. In this paper, we propose a method called DualDeblur, which uses dual blurry images to generate a single sharp image. DualDeblur jointly utilizes the complementary information of multiple blurry images to capture image statistics for a single sharp image. Additionally, we propose an adaptive L2_SSIM loss that enhances both pixel accuracy and structural properties. Extensive experiments show the superior performance of our method to previous methods in both qualitative and quantitative evaluations.

scholarly journals Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 52
Author(s):  
Richard Evan Sutanto ◽  
Sukho Lee
Keyword(s):  
Neural Network ◽  
Attack Detection ◽  
Detection Methods ◽  
Defense System ◽  
Image Prior ◽  
The Neural Network ◽  
Adversarial Examples ◽  
Deep Image ◽  
Adversarial Attack ◽  
High Level

Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.

Delving into Deep Image Prior for Adversarial Defense

2021 ◽  
Author(s):  
Li Ding ◽  
Yongwei Wang ◽  
Xin Ding ◽  
Kaiwen Yuan ◽  
Ping Wang ◽  
...  
Keyword(s):  
Image Prior ◽  
Deep Image

scholarly journals Blind Image Deblurring Based on Local Edges Selection

2019 ◽  
Vol 9 (16) ◽  
pp. 3274
Author(s):  
Han ◽  
Kan
Keyword(s):  
Image Deblurring ◽  
Color Images ◽  
Clear Image ◽  
Blind Image Deblurring ◽  
Alternating Direction ◽  
Dark Channel ◽  
The Difference ◽  
And Performance ◽  
Image Edges ◽  
Blind Image

The edges of images are less sparse when images become blurred. Selecting effective image edges is a vital step in image deblurring, which can help us to build image deblurring models more accurately. While global edges selection methods tend to fail in capturing dense image structures, the edges are easy to be affected by noise and blur. In this paper, we propose an image deblurring method based on local edges selection. The local edges are selected by the difference between the bright channel and the dark channel. Then a novel image deblurring model including local edges regularization term is established. The obtaining of a clear image and blurring kernel is based on alternating iterations, in which the clear image is obtained by the alternating direction method of multipliers (ADMM). In the experiments, tests are carried out on gray value images, synthetic color images and natural color images. Compared with other state-of-the-art blind image deblurring methods, the visualization results and performance verify the effectiveness of our method.

scholarly journals Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging

2021 ◽  
pp. 1-1
Author(s):  
Fangshu Yang ◽  
Thanh-an Pham ◽  
Nathalie Brandenberg ◽  
Matthias P. Lutolf ◽  
Jianwei Ma ◽  
...  
Keyword(s):  
Phase Unwrapping ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Image Prior ◽  
Deep Image
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