scholarly journals Region Filling with Super Resolution Algorithm

2021 ◽  
Vol 9 (VI) ◽  
pp. 3557-3564
Author(s):  
Darakhshan R. Khan
Keyword(s):  
Patch Size ◽  
Research Work ◽  
Super Resolution ◽  
Input Image ◽  
Computational Time ◽  
Low Resolution ◽  
Resolution Image ◽  
Resolution Algorithm ◽  
Region Filling

Region filling which has another name inpainting, is an approach to find the values of missing pixels from data available in the remaining portion of the image. The missing information must be recalculated in a distinctly convincing manner, such that, image look seamless. This research work has built a methodology for completely automating patch priority based region filling process. To reduce the computational time, low resolution image is constructed from input image. Based on texel of an image, patch size is determined. Several low resolution image with missing region filled is generated using region filling algorithm. Pixel information from these low resolution images is consolidated to produce single low resolution region filled image. Finally, super resolution algorithm is applied to enhance the quality of image and regain all specifics of image. This methodology of identifying patch size based on input fed has an advantage over filling algorithms which in true sense automate the process of region filling, to deal with sensitivity in region filling, algorithm different parameter settings are used and functioning with coarse version of image will notably reduce the computational time.

scholarly journals Improved Sparse Representation Super-Resolution algorithm for Remote Sensing Image

2018 ◽  
Vol 232 ◽  
pp. 02040
Author(s):  
Fuzhen Zhu ◽  
Xin Huang ◽  
Yue Liu ◽  
Haitao Zhu
Keyword(s):  
Remote Sensing ◽  
Sparse Representation ◽  
Super Resolution ◽  
Objective Evaluation ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
Resolution Image ◽  
Training Images ◽  
Feature Extract ◽  
Resolution Algorithm

In order to obtain higher quality super-resolution reconstruction (SRR) of remote sensing images, an improved sparse representation remote sensing images SRR method is proposed in this paper. First, low-resolution image is processed by improved feature extract operator. The high-resolution image and low-resolution image blocks have the same sparse representation coefficient, so the SRR image with higher spatial resolution can be derived from the sparse representation coefficients which have been obtained from low-resolution image. The improved feature extraction operator is a method to get more detail and texture information from the training images. Experiment results show that more texture details can be obtained in the result of SRR remote sensing images subjectively. At the same time, the objective evaluation parameters are improved greatly. The peak PSNR is increased about 2.50dB and 0.50 dB, RMSE is decreased about 2.80 and 0.3 compared with bicubic interpolation algorithm and Ref[8] algorithm respectively.

scholarly journals Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Nur-A- Alam ◽  
Mominul Ahsan ◽  
Md. Abdul Based ◽  
Julfikar Haider
Keyword(s):  
Traffic Control ◽  
Industrial Revolution ◽  
Intelligent System ◽  
Research Work ◽  
Digital Camera ◽  
Super Resolution ◽  
Traffic Monitoring ◽  
Input Image ◽  
Resolution Image ◽  
On The Road

Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.

scholarly journals Multi-scale Xception based depthwise separable convolution for single image super-resolution

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0249278
Author(s):  
Wazir Muhammad ◽  
Supavadee Aramvith ◽  
Takao Onoye
Keyword(s):  
Research Work ◽  
Super Resolution ◽  
Single Image ◽  
Low Resolution ◽  
Significant Drop ◽  
Great Ability ◽  
Resolution Image ◽  
Significant Performance ◽  
Image Super Resolution ◽  
Single Image Super Resolution

The main target of Single image super-resolution is to recover high-quality or high-resolution image from degraded version of low-quality or low-resolution image. Recently, deep learning-based approaches have achieved significant performance in image super-resolution tasks. However, existing approaches related with image super-resolution fail to use the features information of low-resolution images as well as do not recover the hierarchical features for the final reconstruction purpose. In this research work, we have proposed a new architecture inspired by ResNet and Xception networks, which enable a significant drop in the number of network parameters and improve the processing speed to obtain the SR results. We are compared our proposed algorithm with existing state-of-the-art algorithms and confirmed the great ability to construct HR images with fine, rich, and sharp texture details as well as edges. The experimental results validate that our proposed approach has robust performance compared to other popular techniques related to accuracy, speed, and visual quality.

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

scholarly journals Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

2021 ◽  
Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke
Keyword(s):  
High Resolution ◽  
Digital Image Correlation ◽  
Spatial Resolution ◽  
Digital Image ◽  
Super Resolution ◽  
Image Correlation ◽  
Low Resolution ◽  
Strain Measurements ◽  
Resolution Image ◽  
High Resolution Image

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

A Video Super Resolution Algorithm Based on Wavelet Coefficient

2014 ◽  
Vol 610 ◽  
pp. 425-428
Author(s):  
Wei Jian Liu ◽  
Si Da Xiao ◽  
Ruo He Yao
Keyword(s):  
High Frequency ◽  
Wavelet Coefficient ◽  
Three Dimensional ◽  
Super Resolution ◽  
Structural Similarity ◽  
Image Sequences ◽  
Discrete Wavelet ◽  
Resolution Image ◽  
Resolution Algorithm ◽  
Visual Results

In this paper, we propose a new super-resolution algorithm based on wavelet coefficient. The proposed algorithm uses discrete wavelet transform (DWT) to decompose the input low-resolution image sequences into four subband images, including LL, LH, HL, HH. Then the input images have been processed by the 3DSKR (Three Dimensional Steering Kernel Regression) super resolution (SR) algorithm, and the result replaces the LL subband image, while the three high-frequency subband images have been interpolated. Finally, combining all these images to generate a new high-resolution image by using inverse DWT. Proposed method has been verified on Calendar and Foliage by Matlab software platform. The peak signal-to-noise (PSNR), structural similarity (SSIM) and visual results are compared, and show that the computational complexity of the proposed algorithm decline by 30 percent compared with the existing algorithm to obtain the approximate results.

scholarly journals A Review on Deep Image Contrast Enhancement

2020 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Image Enhancement ◽  
Research Work ◽  
Atmospheric Condition ◽  
Input Image ◽  
Learning Approaches ◽  
Sensing Applications ◽  
Image Contrast Enhancement

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

Distillation

2010 ◽  
pp. 244-264
Author(s):  
Dong Seon Cheng ◽  
Marco Cristani ◽  
Vittorio Murino
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Video Data ◽  
Distillation Process ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image ◽  
Image Super Resolution ◽  
Comparative Results ◽  
Low Resolution Images

Image super-resolution is one of the most appealing applications of image processing, capable of retrieving a high resolution image by fusing several registered low resolution images depicting an object of interest. However, employing super-resolution in video data is challenging: a video sequence generally contains a lot of scattered information regarding several objects of interest in cluttered scenes. Especially with hand-held cameras, the overall quality may be poor due to low resolution or unsteadiness. The objective of this chapter is to demonstrate why standard image super-resolution fails in video data, which are the problems that arise, and how we can overcome these problems. In our first contribution, we propose a novel Bayesian framework for super-resolution of persistent objects of interest in video sequences. We call this process Distillation. In the traditional formulation of the image super-resolution problem, the observed target is (1) always the same, (2) acquired using a camera making small movements, and (3) found in a number of low resolution images sufficient to recover high-frequency information. These assumptions are usually unsatisfied in real world video acquisitions and often beyond the control of the video operator. With Distillation, we aim to extend and to generalize the image super-resolution task, embedding it in a structured framework that accurately distills all the informative bits of an object of interest. In practice, the Distillation process: i) individuates, in a semi supervised way, a set of objects of interest, clustering the related video frames and registering them with respect to global rigid transformations; ii) for each one, produces a high resolution image, by weighting each pixel according to the information retrieved about the object of interest. As a second contribution, we extend the Distillation process to deal with objects of interest whose transformations in the appearance are not (only) rigid. Such process, built on top of the Distillation, is hierarchical, in the sense that a process of clustering is applied recursively, beginning with the analysis of whole frames, and selectively focusing on smaller sub-regions whose isolated motion can be reasonably assumed as rigid. The ultimate product of the overall process is a strip of images that describe at high resolution the dynamics of the video, switching between alternative local descriptions in response to visual changes. Our approach is first tested on synthetic data, obtaining encouraging comparative results with respect to known super-resolution techniques, and a good robustness against noise. Second, real data coming from different videos are considered, trying to solve the major details of the objects in motion.

scholarly journals Super-resolution reconstruction for a single image based on self-similarity and compressed sensing

2018 ◽  
Vol 12 (3) ◽  
pp. 234-244
Author(s):  
Qiang Yang ◽  
Huajun Wang
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Single Image ◽  
Low Resolution ◽  
Self Similarity ◽  
Resolution Image ◽  
Reconstruction Performance

Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image’s self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse representation and reconstruction algorithm in compressed sensing theory, super-resolution reconstruction (SRSR) of a single image was realized. The proposed algorithm adopted improved K-SVD algorithm for sample training and learning dictionary construction; additionally, the matching pursuit algorithm was improved for achieving single-image SRSR based on image’s self-similarity and compressed sensing. The experimental results reveal that the proposed reconstruction algorithm shows better visual effect and image quality than the degraded low-resolution image; moreover, compared with the reconstructed images using bilinear interpolation and sparse-representation-based algorithms, the reconstructed image using the proposed algorithm has a higher PSNR value and thus exhibits more favorable super-resolution image reconstruction performance.

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