Research on Very Low Resolution Face Image Reconstruction

2014 ◽  
Vol 687-691 ◽  
pp. 3747-3750
Author(s):  
Zhi Zhuang Guo ◽  
Xiao Ling Wang
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Super Resolution ◽  
Face Image ◽  
Image Space ◽  
Low Resolution ◽  
Long Distance ◽  
The Face ◽  
The Relationship ◽  
New Algorithms

The resolution of the face image in video may lower than 16*16 in environmental such as ultra long distance, poor illumination and so on, with the very low resolution (VLR) face image the existing face super-resolution (SR) methods do not perform well. In this paper, we proposes a new algorithms by learning the relationship between high-resolution (HR) image space and the VLR image space for face SR.A new constrain, new data constrain are design for reconstruct HR face image form VLR face image. The Experiment results show that the proposed method can recover a clear face image from the VLR face image.

scholarly journals Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mahmoud M. Khattab ◽  
Akram M. Zeki ◽  
Ali A. Alwan ◽  
Belgacem Bouallegue ◽  
Safaa S. Matter ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Synthetic Data ◽  
Stable State ◽  
Super Resolution ◽  
Low Resolution ◽  
Edge Preservation ◽  
Resolution Image ◽  
High Resolution Image ◽  
L2 Norm

The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.

scholarly journals A Discretize-then-Optimize Approach to Super-Resolution Reconstruction and Motion Estimation

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Eric Ng ◽  
Mehran Ebrahimi
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Motion Estimation ◽  
Test Data ◽  
Super Resolution ◽  
Inverse Model ◽  
Low Resolution ◽  
Coupled Problem ◽  
Resolution Problem

<p>The process of recovering a high-resolution (HR) image from a set<br />of distorted (i.e. deformed, blurry, noisy, etc.) low-resolution (LR) images<br />is known as super-resolution. Super-resolution problem will require<br />the reconstruction of the HR image and estimations of motion<br />between LR images. In this study, image reconstruction and motion<br />estimation will be treated as a coupled problem. The proposed algorithm<br />uses an inverse model followed by a discretize-then-optimize<br />approach. Preliminary experiments on test data will be presented.</p>

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.

Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

2021 ◽  
pp. 1-15
Author(s):  
Yongjie Chu ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
High Resolution ◽  
Domain Adaptation ◽  
Face Image ◽  
Discriminative Learning ◽  
Low Resolution ◽  
Face Images ◽  
World Environment

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.

scholarly journals Shuffle block SRGAN for face image super-resolution reconstruction

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1429-1439
Author(s):  
Ziwei Zhang ◽  
Yangjing Shi ◽  
Xiaoshi Zhou ◽  
Hongfei Kan ◽  
Juan Wen
Keyword(s):  
Face Recognition ◽  
Learning Strategy ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Super Resolution ◽  
Face Image ◽  
Low Resolution ◽  
Index Method ◽  
Face Features ◽  
Image Super Resolution

When low-resolution face images are used for face recognition, the model accuracy is substantially decreased. How to recover high-resolution face features from low-resolution images precisely and efficiently is an essential subtask in face recognition. In this study, we introduce shuffle block SRGAN, a new image super-resolution network inspired by the SRGAN structure. By replacing the residual blocks with shuffle blocks, we can achieve efficient super-resolution reconstruction. Furthermore, by considering the generated image quality in the loss function, we can obtain more realistic super-resolution images. We train and test SB-SRGAN in three public face image datasets and use transfer learning strategy during the training process. The experimental results show that shuffle block SRGAN can achieve desirable image super-resolution performance with respect to visual effect as well as the peak signal-to-noise ratio and structure similarity index method metrics, compared with the performance attained by the other chosen deep-leaning models.

scholarly journals Crop Disease Classification on Inadequate Low-Resolution Target Images

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4601
Author(s):  
Juan Wen ◽  
Yangjing Shi ◽  
Xiaoshi Zhou ◽  
Yiming Xue
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Classification Performance ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Training Samples ◽  
Crop Disease ◽  
High Resolution Images ◽  
Model Training

Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.

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.

Sign in / Sign up

Export Citation Format

Share Document