Optimum Color and Contrast Enhancement for Online Ferrography Image Restoration

2019 ◽  
Vol 2 (3) ◽  
Author(s):  
Lingfeng Yang ◽  
Tonghai Wu ◽  
Kunpeng Wang ◽  
Hongkun Wu ◽  
Ngaiming Kwok
Keyword(s):  
Image Restoration ◽  
Contrast Enhancement ◽  
Real Time ◽  
Lagrange Multiplier ◽  
Information Loss ◽  
Cost Functions ◽  
High Quality ◽  
Degraded Image ◽  
Restoration Method ◽  
The Cost

Online ferrography, because of its nondestructive and real-time capability, has been increasingly applied in monitoring machine wear states. However, online ferrography images are usually degraded as a result of undesirable image acquisition conditions, which eventually lead to inaccurate identifications. A restoration method focusing on color correction and contrast enhancement is developed to provide high-quality images for subsequent processing. Based on the formation of a degraded image, a model describing the degradation is constructed. Then, cost functions consisting of colorfulness, contrast, and information loss are formulated. An optimal restored image is obtained by minimizing the cost functions, in which parameters are properly determined using the Lagrange multiplier. Experiments are carried out on a collection of online ferrography images, and results show that the proposed method can effectively improve the image both qualitatively and quantitatively.

scholarly journals Interactive Entertainment Image Restoration Method Based on Hopfield Neural Network

2018 ◽  
Vol 232 ◽  
pp. 01008
Author(s):  
Shuangqing lv
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Hopfield Neural Network ◽  
Original Data ◽  
Small Scale ◽  
Interactive Entertainment ◽  
Degraded Image ◽  
Restoration Method ◽  
Traditional Image ◽  
Better Than

The traditional image restoration methods of interactive entertainment are based on the original data. This paper proposes an interactive entertainment image restoration method based on Hopfield neural network. Firstly, the nonlinear mapping relationship between the degraded image and the real image is preliminarily established through the network, and then optimized by the algorithm. Finally, the image restoration can be achieved through the network. The experiments show that it has higher feasibility and the recovery effect on small-scale blur is better than the existing method.

Multiframe Turbulence-Degraded Image Restoration Method Based on Temporal Signature

2013 ◽  
Vol 50 (12) ◽  
pp. 120101
Author(s):  
邵慧 Shao Hui ◽  
汪建业 Wang Jianye ◽  
徐鹏 Xu Peng ◽  
杨明翰 Yang Minghan
Keyword(s):  
Image Restoration ◽  
Degraded Image ◽  
Restoration Method

scholarly journals A Novel Real-Time Image Restoration Algorithm in Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xingmin Ma ◽  
Shenggang Xu ◽  
Fengping An ◽  
Fuhong Lin
Keyword(s):  
Image Restoration ◽  
Real Time ◽  
Edge Computing ◽  
Support Vector ◽  
Original Image ◽  
Edge Node ◽  
Restoration Algorithm ◽  
Real Time Image ◽  
Degraded Image ◽  
Time Image

Owning to the high processing complexity, the image restoration can only be processed offline and hardly be applied in the real-time production life. The development of edge computing provides a new solution for real-time image restoration. It can upload the original image to the edge node to process in real time and then return results to users immediately. However, the processing capacity of the edge node is still limited which requires a lightweight image restoration algorithm. A novel real-time image restoration algorithm is proposed in edge computing. Firstly, 10 classical functions are used to determine the population size and maximum iteration times of traction fruit fly optimization algorithm (TFOA). Secondly, TFOA is used to optimize the optimal parameters of least squares support vector regression (LSSVR) kernel function, and the error function of image restoration is taken as an adaptive function of TFOA. Thirdly, the LLSVR algorithm is used to restore the image. During the image restoration process, the training process is to establish a mapping relationship between the degraded image and the adjacent pixels of the original image. The relationship is established; the degraded image can be restored by using the mapping relationship. Through the comparison and analysis of experiments, the proposed method can meet the requirements of real-time image restoration, and the proposed algorithm can speed up the image restoration and improve the image quality.

scholarly journals On a Nonstationary Route Problem with Constraints

2015 ◽  
Vol 19 (4) ◽  
pp. 5-24 ◽  
Author(s):  
A. G. Chentsov ◽  
P. A. Chentsov
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Initial Point ◽  
Programming Method ◽  
Cost Functions ◽  
Dynamic Programming Method ◽  
The Real ◽  
Route Problem ◽  
Regular Succession ◽  
The Cost

The extremal route problem of permutations under constraints in the form of preceding conditions is investigated. It is supposed that an executer leaves the initial point (the base) after which he visits a system of megalopolises (finite goal sets) and performs some work on each megalopolis. The cost functions for executor permutations and interior works depend on the “visiting moment” that can correspond to the real time or can also correspond to the natural regular succession (the first visiting, the second visiting, and so on). An economic variant of the widely interpreted dynamic programming method (DPM) is constructed. On this basis an optimal computer realized algorithm is constructed. A variant of a greed algorithm is proposed.

Real-Time Nonlinear Programming by Amplitude Modulation

2008 ◽  
Author(s):  
Kyoungchul Kong ◽  
Kiyonori Inaba ◽  
Masayoshi Tomizuka
Keyword(s):  
Nonlinear Programming ◽  
Cost Function ◽  
Real Time ◽  
Amplitude Modulation ◽  
Extremum Seeking ◽  
Cost Functions ◽  
Variable Cost ◽  
The Cost ◽  
Variable Cost Function ◽  
Real Time Optimization

Nonlinear Programming (NLP) is for optimization of nonlinear cost functions. In applications of NLP for real-time optimization, however, the estimation of the gradient of the cost function remains as a challenge. On the other hand, the Extremum-Seeking Control (ESC) optimizes the cost function in real-time, but it involves a complicated design of filters in multi-dimensional cases. In this paper, a new method that optimizes an arbitrary multi-variable cost function in real-time is proposed. In the proposed method, the variables are updated as in NLP while the gradient of the cost function is continuously estimated by the amplitude modulation as in ESC. The proposed method does not require design of any complicated filters. The performance is verified by simulations on time-varying and noisy cost functions as well as automatic controller tuning applications.

T2FRF Filter: An Effective Algorithm for the Restoration of Fingerprint Images

2021 ◽  
Author(s):  
Joycy K. Antony ◽  
K. Kanagalakshmi
Keyword(s):  
Image Restoration ◽  
Correlation Coefficient ◽  
Signal To Noise Ratio ◽  
Effective Algorithm ◽  
Main Challenge ◽  
Wide Range ◽  
Noisy Pixel ◽  
Degraded Image ◽  
Dim Light ◽  
Restoration Method

Images captured in dim light are hardly satisfactory and increasing the International Organization for Standardization (ISO) for a short duration of exposure makes them noisy. The image restoration methods have a wide range of applications in the field of medical imaging, computer vision, remote sensing, and graphic design. Although the use of flash improves the lighting, it changed the image tone besides developing unnecessary highlight and shadow. Thus, these drawbacks are overcome using the image restoration methods that recovered the image with high quality from the degraded observation. The main challenge in the image restoration approach is recovering the degraded image contaminated with the noise. In this research, an effective algorithm, named T2FRF filter, is developed for the restoration of the image. The noisy pixel is identified from the input fingerprint image using Deep Convolutional Neural Network (Deep CNN), which is trained using the neighboring pixels. The Rider Optimization Algorithm (ROA) is used for the removal of the noisy pixel in the image. The enhancement of the pixel is performed using the type II fuzzy system. The developed T2FRF filter is measured using the metrics, such as correlation coefficient and Peak Signal to Noise Ratio (PSNR) for evaluating the performance. When compared with the existing image restoration method, the developed method obtained a maximum correlation coefficient of 0.7504 and a maximum PSNR of 28.2467dB, respectively.

scholarly journals Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4190 ◽  
Author(s):  
Saad Rizvi ◽  
Jie Cao ◽  
Kaiyu Zhang ◽  
Qun Hao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Quality ◽  
Imaging Quality ◽  
Convolutional Autoencoder ◽  
Image Set ◽  
Real Time Applications ◽  
The Cost ◽  
Single Pixel

Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5–8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.

scholarly journals A Review on Image Contrast Enhancement Techniques

2019 ◽  
Vol 5 (7) ◽  
pp. 5
Author(s):  
Pooja Patel ◽  
Arpana Bhandari
Keyword(s):  
Image Restoration ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Research Work ◽  
Image Features ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Restoration Techniques ◽  
Degraded Image

The purpose of image enhancement and image restoration techniques is to perk up a quality and feature of an image that result in improved image than the original one. Unlike the image restoration, image enhancement is the modification of an image to alter impact on the viewer. Generally enhancement distorts the original digital values; therefore enhancement is not done until the restoration processes are completed. In image enhancement the image features are extracted instead of restoration of degraded image. Image enhancement is the process in which the degraded image is handled and the appearance of the image by visual is improved. It is a subjective process and increases contrast of image but image restoration is a more objective process than image enhancement. Many research work have been done for image enhancement. In this paper, different techniques and algorithms are discussed for contrast enhancement.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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