scholarly journals OCT Image Restoration Using Non-Local Deep Image Prior

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 784
Author(s):  
Wenshi Fan ◽  
Hancheng Yu ◽  
Tianming Chen ◽  
Sheng Ji
Keyword(s):  
Image Restoration ◽  
Image Denoising ◽  
Superior Performance ◽  
Quality Data ◽  
Speckle Reduction ◽  
Low Level ◽  
Image Prior ◽  
Non Local ◽  
Deep Image ◽  
Oct Image

In recent years, convolutional neural networks (CNN) have been widely used in image denoising for their high performance. One difficulty in applying the CNN to medical image denoising such as speckle reduction in the optical coherence tomography (OCT) image is that a large amount of high-quality data is required for training, which is an inherent limitation for OCT despeckling. Recently, deep image prior (DIP) networks have been proposed for image restoration without pre-training since the CNN structures have the intrinsic ability to capture the low-level statistics of a single image. However, the DIP has difficulty finding a good balance between maintaining details and suppressing speckle noise. Inspired by DIP, in this paper, a sorted non-local statics which measures the signal autocorrelation in the differences between the constructed image and the input image is proposed for OCT image restoration. By adding the sorted non-local statics as a regularization loss in the DIP learning, more low-level image statistics are captured by CNN networks in the process of OCT image restoration. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual quality.

scholarly journals Image Denoising Using Nonlocal Regularized Deep Image Prior

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2114
Author(s):  
Zhonghua Xie ◽  
Lingjun Liu ◽  
Zhongliang Luo ◽  
Jianfeng Huang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Denoising ◽  
Deep Neural Networks ◽  
Optimization Problems ◽  
Training Data ◽  
Superior Performance ◽  
Convolutional Network ◽  
Image Prior ◽  
Deep Image

Deep neural networks have shown great potential in various low-level vision tasks, leading to several state-of-the-art image denoising techniques. Training a deep neural network in a supervised fashion usually requires the collection of a great number of examples and the consumption of a significant amount of time. However, the collection of training samples is very difficult for some application scenarios, such as the full-sampled data of magnetic resonance imaging and the data of satellite remote sensing imaging. In this paper, we overcome the problem of a lack of training data by using an unsupervised deep-learning-based method. Specifically, we propose a deep-learning-based method based on the deep image prior (DIP) method, which only requires a noisy image as training data, without any clean data. It infers the natural images with random inputs and the corrupted observation with the help of performing correction via a convolutional network. We improve the original DIP method as follows: Firstly, the original optimization objective function is modified by adding nonlocal regularizers, consisting of a spatial filter and a frequency domain filter, to promote the gradient sparsity of the solution. Secondly, we solve the optimization problem with the alternating direction method of multipliers (ADMM) framework, resulting in two separate optimization problems, including a symmetric U-Net training step and a plug-and-play proximal denoising step. As such, the proposed method exploits the powerful denoising ability of both deep neural networks and nonlocal regularizations. Experiments validate the effectiveness of leveraging a combination of DIP and nonlocal regularizers, and demonstrate the superior performance of the proposed method both quantitatively and visually compared with the original DIP method.

scholarly journals Dynamic PET Image Denoising Using Deep Image Prior Combined With Regularization by Denoising

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 52378-52392
Author(s):  
Hao Sun ◽  
Lihong Peng ◽  
Hongyan Zhang ◽  
Yuru He ◽  
Shuangliang Cao ◽  
...  
Keyword(s):  
Image Denoising ◽  
Dynamic Pet ◽  
Image Prior ◽  
Deep Image

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 Unsupervised Adversarial Defense through Tandem Deep Image Priors

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1957
Author(s):  
Yu Shi ◽  
Cien Fan ◽  
Lian Zou ◽  
Caixia Sun ◽  
Yifeng Liu
Keyword(s):  
Image Restoration ◽  
Deep Neural Networks ◽  
Defense Mechanism ◽  
Original Image ◽  
Image Prior ◽  
Wrong Prediction ◽  
Previous State ◽  
Deep Image ◽  
Image Priors ◽  
Adversarial Example

Deep neural networks are vulnerable to the adversarial example synthesized by adding imperceptible perturbations to the original image but can fool the classifier to provide wrong prediction outputs. This paper proposes an image restoration approach which provides a strong defense mechanism to provide robustness against adversarial attacks. We show that the unsupervised image restoration framework, deep image prior, can effectively eliminate the influence of adversarial perturbations. The proposed method uses multiple deep image prior networks called tandem deep image priors to recover the original image from adversarial example. Tandem deep image priors contain two deep image prior networks. The first network captures the main information of images and the second network recovers original image based on the prior information provided by the first network. The proposed method reduces the number of iterations originally required by deep image prior network and does not require adjusting the classifier or pre-training. It can be combined with other defensive methods. Our experiments show that the proposed method surprisingly achieves higher classification accuracy on ImageNet against a wide variety of adversarial attacks than previous state-of-the-art defense methods.

Image Restoration with Structured Deep Image Prior

2021 ◽  
Author(s):  
Jikai Li ◽  
Ruiki Kobayashi ◽  
Shogo Muramatsu ◽  
Gwanggil Jeon
Keyword(s):  
Image Restoration ◽  
Image Prior ◽  
Deep Image
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