scholarly journals MRI Superresolution Using Self-Similarity and Image Priors

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
José V. Manjón ◽  
Pierrick Coupé ◽  
Antonio Buades ◽  
D. Louis Collins ◽  
Montserrat Robles
Keyword(s):  
High Resolution ◽  
Real Data ◽  
Clinical Settings ◽  
Low Resolution ◽  
Self Similarity ◽  
Reconstruction Process ◽  
Classical State ◽  
High Resolution Images ◽  
Interpolation Techniques ◽  
Improved Performance

In Magnetic Resonance Imaging typical clinical settings, both low- and high-resolution images of different types are routinarily acquired. In some cases, the acquired low-resolution images have to be upsampled to match with other high-resolution images for posterior analysis or postprocessing such as registration or multimodal segmentation. However, classical interpolation techniques are not able to recover the high-frequency information lost during the acquisition process. In the present paper, a new superresolution method is proposed to reconstruct high-resolution images from the low-resolution ones using information from coplanar high resolution images acquired of the same subject. Furthermore, the reconstruction process is constrained to be physically plausible with the MR acquisition model that allows a meaningful interpretation of the results. Experiments on synthetic and real data are supplied to show the effectiveness of the proposed approach. A comparison with classical state-of-the-art interpolation techniques is presented to demonstrate the improved performance of the proposed methodology.

Hands Stencils in El Castillo Cave (Puente Viesgo, Cantabria, Spain). An Interdisciplinary Study

2021 ◽  
pp. 1-21
Author(s):  
Sergio Ripoll ◽  
Vicente Bayarri ◽  
Francisco J. Muñoz ◽  
Ricardo Ortega ◽  
Elena Castillo ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Data ◽  
Digital Processing ◽  
The Other ◽  
Major Step ◽  
The People ◽  
High Resolution Images ◽  
Real Picture ◽  
Interdisciplinary Study ◽  
Very High

Our Palaeolithic ancestors did not make good representations of themselves on the rocky surfaces of caves and barring certain exceptions – such as the case of La Marche (found on small slabs of stone or plaquettes) or the Cueva de Ambrosio – the few known examples can only be referred to as anthropomorphs. As such, only hand stencils give us a real picture of the people who came before us. Hand stencils and imprints provide us with a large amount of information that allows us to approach not only their physical appearance but also to infer less tangible details, such as the preferential use of one hand over the other (i.e., handedness). Both new and/or mature technologies as well as digital processing of images, computers with the ability to process very high resolution images, and a more extensive knowledge of the Palaeolithic figures all help us to analyse thoroughly the hands in El Castillo cave. The interdisciplinary study presented here contributes many novel developments based on real data, representing a major step forward in knowledge about our predecessors.

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.

Stereo Matching Algorithm Based on Pyramid Double Dynamic Programming

2014 ◽  
Vol 981 ◽  
pp. 352-355 ◽  
Author(s):  
Ji Zhou Wei ◽  
Shu Chun Yu ◽  
Wen Fei Dong ◽  
Chao Feng ◽  
Bing Xie
Keyword(s):  
Dynamic Programming ◽  
High Resolution ◽  
Stereo Matching ◽  
Control Points ◽  
Low Resolution ◽  
Matching Algorithm ◽  
Resolution Image ◽  
High Resolution Images ◽  
Candidate Control ◽  
Low Resolution Images

A stereo matching algorithm was proposed based on pyramid algorithm and dynamic programming. High and low resolution images was computed by pyramid algorithm, and then candidate control points were stroke on low-resolution image, and final control points were stroke on the high-resolution images. Finally, final control points were used in directing stereo matching based on dynamic programming. Since the striking of candidate control points on low-resolution image, the time is greatly reduced. Experiments show that the proposed method has a high matching precision.

Focused Plenoptic Camera and Spatial Resolution Improving Technology

2013 ◽  
Vol 710 ◽  
pp. 419-423
Author(s):  
Juan Ning Zhao ◽  
Xiao Na Dong ◽  
Suo Chao Yuan
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Traditional Approach ◽  
Microlens Array ◽  
Low Resolution ◽  
New Approach ◽  
Plenoptic Camera ◽  
High Resolution Images

The focused plenoptic cameras based on the rays resampling of microlens array on the image formed by main lens, captures radiation on sensor includes the 4D radiance information.Because of both spatial and angular information are recorded on the sensor of fixed pixels number, when rendering image with fixed view there are limited pixels from sub_image are adopted, this results in disappointingly low resolution of the result image. Our approach presents a new approach to rendering an image with higher spatial resolution than the traditional approach, allowing us to render high resolution images that meet the high requirements.

scholarly journals Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

2018 ◽  
Author(s):  
Zheng Wang ◽  
Mang Ye ◽  
Fan Yang ◽  
Xiang Bai ◽  
Shin'ichi Satoh
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Feature Representation ◽  
Image Feature ◽  
Low Resolution ◽  
Open World ◽  
In Series ◽  
High Resolution Images ◽  
Public Dataset ◽  
Image Super Resolution

Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SR-GAN is one of the most competitive image super-resolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.

scholarly journals Thick Cloud Removal in High-Resolution Satellite Images Using Stepwise Radiometric Adjustment and Residual Correction

2019 ◽  
Vol 11 (16) ◽  
pp. 1925 ◽  
Author(s):  
Zhiwei Li ◽  
Huanfeng Shen ◽  
Qing Cheng ◽  
Wei Li ◽  
Liangpei Zhang
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Real Data ◽  
Target Image ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Residual Correction ◽  
Geographical Maps ◽  
Cloud Removal ◽  
Cloud Mask

Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images as well as a reduction in the data usability. In this paper, a thick cloud removal method based on stepwise radiometric adjustment and residual correction (SRARC) is proposed, which is aimed at effectively removing the clouds in high-resolution images for the generation of high-quality and spatially contiguous urban geographical maps. The basic idea of SRARC is that the complementary information in adjacent temporal satellite images can be utilized for the seamless recovery of cloud-contaminated areas in the target image after precise radiometric adjustment. To this end, the SRARC method first optimizes the given cloud mask of the target image based on superpixel segmentation, which is conducted to ensure that the labeled cloud boundaries go through homogeneous areas of the target image, to ensure a seamless reconstruction. Stepwise radiometric adjustment is then used to adjust the radiometric information of the complementary areas in the auxiliary image, step by step, and clouds in the target image can be removed by the replacement with the adjusted complementary areas. Finally, residual correction based on global optimization is used to further reduce the radiometric differences between the recovered areas and the cloud-free areas. The final cloud removal results are then generated. High-resolution images with different spatial resolutions and land-cover change patterns were used in both simulated and real-data cloud removal experiments. The results suggest that SRARC can achieve a better performance than the other compared methods, due to the superiority of the radiometric adjustment and spatial detail preservation. SRARC is thus a promising approach that has the potential for routine use, to support applications based on high-resolution satellite images.

Stepped-Frequency Radar Imaging Algorithm Based on Compression Sensing

2014 ◽  
Vol 543-547 ◽  
pp. 2609-2613 ◽  
Author(s):  
Lu Huang ◽  
Peng Yu Wang ◽  
Qian Song
Keyword(s):  
High Resolution ◽  
Ground Penetrating Radar ◽  
Real Data ◽  
Metal Mine ◽  
High Resolution Images ◽  
Resolution Imaging ◽  
Stepped Frequency ◽  
Compression Sensing ◽  
Ground Penetrating ◽  
Forward Looking

Compressive sensing (CS) theory asserts that one can recover original signals from far fewer random samples under the condition of being sparse. CS theory is applied to high resolution imaging of vehicle-mounted stepped-frequency forward-looking ground-penetrating radar. This paper explores an approach of obtaining discrete scattering structure of the metal mine based on CS imaging and extracting geometry parameters to discriminate targets. Real data of vehicle-mounted stepped-frequency forward-looking ground-penetrating radar is processed. High resolution images of the metal mine with double-scattering structure are obtained. The feasibility of the method is tested through these images.

A Review of Super-Resolution Restoration from Image Sequences

2011 ◽  
Vol 204-210 ◽  
pp. 1336-1341
Author(s):  
Zhi Gang Xu ◽  
Xiu Qin Su
Keyword(s):  
High Resolution ◽  
State Of The Art ◽  
Super Resolution ◽  
Image Sequences ◽  
The State ◽  
Color Images ◽  
Low Resolution ◽  
Future Directions ◽  
High Resolution Images ◽  
Multidisciplinary Studies

Super-resolution (SR) restoration produces one or a set of high resolution images from low-resolution observations. In particular, SR restoration involves many multidisciplinary studies. A review on recent SR restoration approaches was given in this paper. First, we introduced the characteristics and framework of SR restoration. The state of the art in SR restoration was surveyed by taxonomy. Then we summarized and analyzed the existing algorithms of registration and reconstruction. A comparison of performing differences between these methods would only be valid given. After that we discussed the SR problems of color images and compressed videos. At last, we concluded with some thoughts about future directions.

An Improved Algorithm for Super-Resolution Video

2015 ◽  
Vol 713-715 ◽  
pp. 1574-1578
Author(s):  
Yan Zhang ◽  
Pan Pan Jiang
Keyword(s):  
High Resolution ◽  
High Frequency ◽  
Gradient Descent ◽  
Super Resolution ◽  
Video Frame ◽  
Matlab Simulation ◽  
Improved Method ◽  
Low Resolution ◽  
High Resolution Images ◽  
Video Reconstruction

Aiming at the characteristics of the UAV camera, camera data nowadays, a new improved method is proposed based on putting the low-resolution video reconstruction into high-resolution video. First, the low-resolution video frame is done spectrum analysis by Fourier transform. Second, find the maximum gradient descent point to determine the cut off frequency. Finally making use of high-resolution images with high frequency detail, then motion compensated. Through POCS algorithm, then iterated, obtaining super-resolution reconstruction video and realizing the above by MATLAB simulation.

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