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.

scholarly journals GANs-based PIV resolution enhancement without the need of high-resolution input

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alejandro Güemes ◽  
Carlos Sanmiguel Vila ◽  
Stefano Discetti
Keyword(s):  
High Resolution ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Turbulent Channel Flow ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Incomplete Projections ◽  
Data Driven Approach ◽  
High Resolution Images

A data-driven approach to reconstruct high-resolution flow fields is presented. The method is based on exploiting the recent advances of SRGANs (Super-Resolution Generative Adversarial Networks) to enhance the resolution of Particle Image Velocimetry (PIV). The proposed approach exploits the availability of incomplete projections on high-resolution fields using the same set of images processed by standard PIV. Such incomplete projection is made available by sparse particle-based measurements such as super-resolution particle tracking velocimetry. Consequently, in contrast to other works, the method does not need a dual set of low/high-resolution images, and can be applied directly on a single set of raw images for training and estimation. This data-enhanced particle approach is assessed employing two datasets generated from direct numerical simulations: a fluidic pinball and a turbulent channel flow. The results prove that this data-driven method is able to enhance the resolution of PIV measurements even in complex flows without the need of a separate high-resolution experiment for training.

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 Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1312
Author(s):  
Debapriya Hazra ◽  
Yung-Cheol Byun
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Super Resolution ◽  
Poor Quality ◽  
Generative Adversarial Networks ◽  
Temporal Consistency ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Benchmark Datasets

Video super-resolution has become an emerging topic in the field of machine learning. The generative adversarial network is a framework that is widely used to develop solutions for low-resolution videos. Video surveillance using closed-circuit television (CCTV) is significant in every field, all over the world. A common problem with CCTV videos is sudden video loss or poor quality. In this paper, we propose a generative adversarial network that implements spatio-temporal generators and discriminators to enhance real-time low-resolution CCTV videos to high-resolution. The proposed model considers both foreground and background motion of a CCTV video and effectively models the spatial and temporal consistency from low-resolution video frames to generate high-resolution videos. Quantitative and qualitative experiments on benchmark datasets, including Kinetics-700, UCF101, HMDB51 and IITH_Helmet2, showed that our model outperforms the existing GAN models for video super-resolution.

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.

scholarly journals Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7903
Author(s):  
Muhammad Hassan Maqsood ◽  
Rafia Mumtaz ◽  
Ihsan Ul Haq ◽  
Uferah Shafi ◽  
Syed Mohammad Hassan Zaidi ◽  
...  
Keyword(s):  
High Resolution ◽  
Yellow Rust ◽  
Super Resolution ◽  
Noise Removal ◽  
Generative Adversarial Networks ◽  
Wheat Crop ◽  
Test Accuracy ◽  
Low Resolution ◽  
Low Resolution Images

Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.

scholarly journals AN EFFICIENT METHOD TO FEED HIGH RESOLUTION IMAGES TO FACIAL ANALYSIS SYSTEMS

2013 ◽  
pp. 68-71
Author(s):  
ROOPA R ◽  
MRS. VANI.K. S ◽  
MRS. NAGAVENI. V
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
High Resolution ◽  
Super Resolution ◽  
Poor Quality ◽  
Low Resolution ◽  
Resolution Method ◽  
Facial Analysis ◽  
Face Images ◽  
High Resolution Images

Image Processing is any form of signal processing for which the image is an input such as a photograph or video frame. The output of image processing may be either an image or a set of characteristics or parameters related to the image. In many facial analysis systems like Face Recognition face is used as an important biometric. Facial analysis systems need High Resolution images for their processing. The video obtained from inexpensive surveillance cameras are of poor quality. Processing of poor quality images leads to unexpected results. To detect face images from a video captured by inexpensive surveillance cameras, we will use AdaBoost algorithm. If we feed those detected face images having low resolution and low quality to face recognition systems they will produce some unstable and erroneous results. Because these systems have problem working with low resolution images. Hence we need a method to bridge the gap between on one hand low- resolution and low-quality images and on the other hand facial analysis systems. Our approach is to use a Reconstruction Based Super Resolution method. In Reconstruction Based Super Resolution method we will generate a face-log containing images of similar frontal faces of the highest possible quality using head pose estimation technique. Then, we use a Learning Based Super-Resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. Hence the total system quality factor will be improved by four.

Image Super-Resolution via Low-Rank Representation

2014 ◽  
Vol 568-570 ◽  
pp. 652-655 ◽  
Author(s):  
Zhao Li ◽  
Le Wang ◽  
Tao Yu ◽  
Bing Liang Hu
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Low Rank ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image ◽  
High Resolution Images ◽  
Low Rank Representation ◽  
Image Super Resolution ◽  
Single Image Super Resolution

This paper presents a novel method for solving single-image super-resolution problems, based upon low-rank representation (LRR). Given a set of a low-resolution image patches, LRR seeks the lowest-rank representation among all the candidates that represent all patches as the linear combination of the patches in a low-resolution dictionary. By jointly training two dictionaries for the low-resolution and high-resolution images, we can enforce the similarity of LLRs between the low-resolution and high-resolution image pair with respect to their own dictionaries. Therefore, the LRR of a low-resolution image can be applied with the high-resolution dictionary to generate a high-resolution image. Unlike the well-known sparse representation, which computes the sparsest representation of each image patch individually, LRR aims at finding the lowest-rank representation of a collection of patches jointly. LRR better captures the global structure of image. Experiments show that our method gives good results both visually and quantitatively.

scholarly journals Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

2021 ◽  
Vol 13 (12) ◽  
pp. 2308
Author(s):  
Masoomeh Aslahishahri ◽  
Kevin G. Stanley ◽  
Hema Duddu ◽  
Steve Shirtliffe ◽  
Sally Vail ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Real World ◽  
Super Resolution ◽  
Aerial Images ◽  
Plant Phenotyping ◽  
Low Resolution ◽  
High Resolution Images ◽  
Synthetic Images ◽  
Low Resolution Images

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.

scholarly journals BFRVSR: A Bidirectional Frame Recurrent Method for Video Super-Resolution

2020 ◽  
Author(s):  
Xiongxiong Xue ◽  
Zhenqi Han ◽  
Weiqin Tong ◽  
Mingqi Li ◽  
Lizhuang Liu
Keyword(s):  
High Resolution ◽  
Computational Cost ◽  
Super Resolution ◽  
Temporal Consistency ◽  
Low Resolution ◽  
Resolution Method ◽  
Resolution Image ◽  
High Resolution Image ◽  
High Resolution Images ◽  
Low Resolution Images

Video super-resolution, which utilizes the relevant information of several low-resolution frames to generate high-resolution images, is a challenging task. One possible solution called sliding window method tries to divide the generation of high-resolution video sequences into independent sub-tasks, and only adjacent low-resolution images are used to estimate the high-resolution version of the central low-resolution image. Another popular method named recurrent algorithm proposes to utilize not only the low-resolution images but also the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former one usually leads to bad temporal consistency and requires higher computational cost while the latter method always can not make full use of information contained by optical flow or any other calculated features. Thus more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, a reverse training is proposed that the generated high-resolution frame is also utilized to help estimate the high-resolution version of the former frame. With the contribution of reverse training and the forward training, the idea of bidirectional recurrent method not only guarantees the temporal consistency but also make full use of the adjacent information due to the bidirectional training operation while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance that it solves the time-related problems when the generated high-resolution image is impressive compared with recurrent-based video super-resolution method.

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