scholarly journals Defocused image restoration method based on micro-nano scale

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Yongjun Liu ◽  
Qiuyu Wu ◽  
Mingxin Zhang ◽  
Yi Wang
Keyword(s):  
Image Restoration ◽  
Enhancement Effect ◽  
Multi Scale ◽  
Nano Scale ◽  
Adaptive Noise ◽  
Restoration Method ◽  
Defocused Image ◽  
Optical Defocus ◽  
Scale Grid

An image adaptive noise reduction enhancement algorithm based on NSCT is proposed to perform image restoration preprocessing on the defocused image obtained under the microscope. Defocused images acquired under micro-nano scale optical microscopy, usually with inconspicuous details, edges and contours, affect the accuracy of subsequent observation tasks. Due to its multi-scale and multi-directionality, the NSCT transform has superior transform functions and can obtain more textures and edges of images. Combined with the characteristics of micro-nanoscale optical defocus images, the NSCT inverse transform is performed on all sub-bands to reconstruct the image. Finally, the experimental results of the standard 500nm scale grid, conductive probe and triangular probe show that the proposed algorithm has a better image enhancement effect and significantly improves the quality of out-of-focus images.

scholarly journals Joint method using Akamatsu and discrete wavelet transform for image restoration

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jihad Maulana Akbar ◽  
De Rosal Ignatius Moses Setiadi
Keyword(s):  
Image Restoration ◽  
Wavelet Transformation ◽  
Digital Content ◽  
Discrete Wavelet ◽  
Additional Noise ◽  
Restoration Method ◽  
Blurred Images ◽  
Salt And Pepper ◽  
Excessive Noise

Current technology makes it easy for humans to take an image and convert it to digital content, but sometimes there is additional noise in the image so it looks damaged. The damage that often occurs, like blurring and excessive noise in digital images, can certainly affect the meaning and quality of the image. Image restoration is a process used to restore the image to its original state before the image damage occurs. In this research, we proposed an image restoration method by combining Wavelet transformation and Akamatsu transformation. Based on previous research Akamatsu's transformation only works well on blurred images. In order not to focus solely on blurry images, Akamatsu's transformation will be applied based on Wavelet transformations on high-low (HL), low-high (LH), and high-high (HH) subunits. The result of the proposed method will be comparable with the previous methods. PSNR is used as a measure of image quality restoration. Based on the results the proposed method can improve the quality of the restoration on image noise, such as Gaussian, salt and pepper, and also works well on blurred images. The average increase is around 2 dB based on the PSNR calculation.

scholarly journals Underwater Image Restoration Based on Image Fusion

2020 ◽  
Vol 9 (2) ◽  
pp. 621-626
Keyword(s):  
Laplace Transform ◽  
Image Fusion ◽  
Image Restoration ◽  
Essential Feature ◽  
Fusion Technique ◽  
Scattering Of Light ◽  
Multi Scale ◽  
Underwater Image ◽  
Image Fusion Technique

The haziness in underwater images occurs due to two major phenomena namely absorption and scattering of light. Hence, we have proposed an image fusion-based approach to improve the visibility of images obtained underwater. The proposed method uses a single hazy image. Initially the colour corrected and contrast improved versions of the image are obtained. Further, Laplace transform is applied which is followed by replication and saliency mapping on each of the derived images. Multi-scale image fusion technique has been used to combine the inputs. This enables each of the fused images to contribute the most essential feature to obtain the resultant image. Thus, the proposed method significantly restores the quality of the input distorted images.

Restoration of Medical Image Distortion Based on Improved Genetic Algorithm in Matlab Environment

2012 ◽  
Vol 538-541 ◽  
pp. 2097-2101
Author(s):  
Ming Zhu Lu ◽  
Si Xiang Zhang ◽  
Ping Hu ◽  
Hai Yi Sun
Keyword(s):  
Genetic Algorithm ◽  
Image Restoration ◽  
Medical Image ◽  
Simulation Experiment ◽  
Image Distortion ◽  
Improved Genetic Algorithm ◽  
Genetic Operator ◽  
Restoration Method ◽  
Fuzzy Image

In the process of imaging, transmission and recording, there would be some decline in the quality of medical image typically displayed as fuzzy image, distorition, noise, and so on. To remedy the defects of traditional genetic algorithm of image restoration, the image restoration method was presented based on coding design, species initialization, genetic operator and update mechanism. The simulation experiment on image distortion caused by motion was also carried out in MATLAB 7.0 environment.

scholarly journals Improving image quality of x-ray in-line phase contrast imaging using an image restoration method

2011 ◽  
Vol 19 (23) ◽  
pp. 23460 ◽  
Author(s):  
Xue-jun Guo ◽  
Xiao-lin Liu ◽  
Chen Ni ◽  
Bo Liu ◽  
Shi-ming Huang ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Phase Contrast ◽  
Phase Contrast Imaging ◽  
Contrast Imaging ◽  
X Ray ◽  
Restoration Method

scholarly journals Image fusion network for dual-modal restoration

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Ying Zhang ◽  
Xuhua Ren ◽  
Bryan Alexander Clifford ◽  
Qian Wang ◽  
Xiaoqun Zhang
Keyword(s):  
Data Storage ◽  
Network Architecture ◽  
Target Image ◽  
High Quality ◽  
Modal Data ◽  
Multi Scale ◽  
Fusion Methods ◽  
Reference Images ◽  
Restoration Method

<p style='text-indent:20px;'>In recent years multi-modal data processing methods have gained considerable research interest as technological advancements in imaging, computing, and data storage have made the collection of redundant, multi-modal data more commonplace. In this work we present an image restoration method tailored for scenarios where pre-existing, high-quality images from different modalities or contrasts are available in addition to the target image. Our method is based on a novel network architecture which combines the benefits of traditional multi-scale signal representation, such as wavelets, with more recent concepts from data fusion methods. Results from numerical simulations in which T1-weighted MRI images are used to restore noisy and undersampled T2-weighted images demonstrate that the proposed network successfully utilizes information from high-quality reference images to improve the restoration quality of the target image beyond that of existing popular methods.</p>

Aerial Transmission Lines Image Restoration Technology Research Based on the Improved Maximum Entropy

2013 ◽  
Vol 401-403 ◽  
pp. 1315-1318
Author(s):  
Bao Shu Li ◽  
Wen Li Wei ◽  
Ke Bin Cui ◽  
Xue Tao Xu
Keyword(s):  
Image Restoration ◽  
Maximum Entropy ◽  
Transmission Lines ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
First Order ◽  
Image Blurring ◽  
Blurred Image ◽  
Restoration Method

According to the limitations of the shooting environment, captured image exist the phenomenon of image blurring and noise. This paper proposes that the improved maximum entropy method recovery blurred image which acquire in aerial. Finally, according to the first order Markoff theory to evaluate the quality of the processed image, the results show that maximum entropy image restoration method compared to the conventional approach increase image clarity and details more better.

scholarly journals Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization

2021 ◽  
Vol 9 (6) ◽  
pp. 570
Author(s):  
Qingliang Jiao ◽  
Ming Liu ◽  
Pengyu Li ◽  
Liquan Dong ◽  
Mei Hui ◽  
...  
Keyword(s):  
Image Restoration ◽  
Qualitative And Quantitative Analysis ◽  
Formation Model ◽  
High Quality ◽  
Thermal Exchange ◽  
Qualitative And Quantitative ◽  
Smooth Variation ◽  
Underwater Image ◽  
Dark Channel

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of underwater images. Finally, the restored image can be estimated via the image formation model. The results show that the proposed algorithm can output high-quality images, according to qualitative and quantitative analysis.

scholarly journals Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1906
Author(s):  
Jia-Zheng Jian ◽  
Tzong-Rong Ger ◽  
Han-Hua Lai ◽  
Chi-Ming Ku ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Network Structure ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Multi Scale ◽  
Imbalance Problem ◽  
Average Accuracy ◽  
Significant Difference ◽  
Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

Sign in / Sign up

Export Citation Format

Share Document