scholarly journals Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiajun Zhu ◽  
Man Cheng ◽  
Qifan Wang ◽  
Hongbo Yuan ◽  
Zhenjiang Cai
Keyword(s):  
Deep Learning ◽  
Image Enhancement ◽  
Network Performance ◽  
Super Resolution ◽  
Black Rot ◽  
Detection Accuracy ◽  
Test Set ◽  
Resolution Image ◽  
Small Targets ◽  
Grape Leaf

The disease spots on the grape leaves can be detected by using the image processing and deep learning methods. However, the accuracy and efficiency of the detection are still the challenges. The convolutional substrate information is fuzzy, and the detection results are not satisfactory if the disease spot is relatively small. In particular, the detection will be difficult if the number of pixels of the spot is <32 × 32 in the image. In order to effectively address this problem, we present a super-resolution image enhancement and convolutional neural network-based algorithm for the detection of black rot on grape leaves. First, the original image is up-sampled and enhanced with local details using the bilinear interpolation. As a result, the number of pixels in the image increase. Then, the enhanced images are fed into the proposed YOLOv3-SPP network for detection. In the proposed network, the IOU (Intersection Over Union, IOU) in the original YOLOv3 network is replaced with GIOU (Generalized Intersection Over Union, GIOU). In addition, we also add the SPP (Spatial Pyramid Pooling, SPP) module to improve the detection performance of the network. Finally, the official pre-trained weights of YOLOv3 are used for fast convergence. The test set test_pv from the Plant Village and the test set test_orchard from the orchard field were used to evaluate the network performance. The results of test_pv show that the grape leaf black rot is detected by the YOLOv3-SPP with 95.79% detection accuracy and 94.52% detector recall, which is a 5.94% greater in terms of accuracy and 10.67% greater in terms of recall as compared to the original YOLOv3. The results of test_orchard show that the method proposed in this paper can be applied in field environment with 86.69% detection precision and 82.27% detector recall, and the accuracy and recall were improved to 94.05 and 93.26% if the images with the simple background. Therefore, the detection method proposed in this work effectively solves the detection task of small targets and improves the detection effectiveness of the grape leaf black rot.

Small Targets Detection for Transmission Tower Based on SRGAN and Faster RCNN

2021 ◽  
Vol 14 ◽  
Author(s):  
Runze Liu ◽  
Guangwei Yan ◽  
Hui He ◽  
Yubin An ◽  
Ting Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Small Object ◽  
Detection Model ◽  
Tower Equipment ◽  
Small Object Detection ◽  
Small Targets

Background: Power line inspection is essential to ensure the safe and stable operation of the power system. Object detection for tower equipment can significantly improve inspection efficiency. However, due to the low resolution of small targets and limited features, the detection accuracy of small targets is not easy to improve. Objective: This study aimed to improve the tiny targets’ resolution while making the small target's texture and detailed features more prominent to be perceived by the detection model. Methods: In this paper, we propose an algorithm that employs generative adversarial networks to improve small objects' detection accuracy. First, the original image is converted into a super-resolution one by a super-resolution reconstruction network (SRGAN). Then the object detection framework Faster RCNN is utilized to detect objects on the super-resolution images. Result: The experimental results on two small object recognition datasets show that the model proposed in this paper has good robustness. It can especially detect the targets missed by Faster RCNN, which indicates that SRGAN can effectively enhance the detailed information of small targets by improving the resolution. Conclusion: We found that higher resolution data is conducive to obtaining more detailed information of small targets, which can help the detection algorithm achieve higher accuracy. The small object detection model based on the generative adversarial network proposed in this paper is feasible and more efficient. Compared with Faster RCNN, this model has better performance on small object detection.

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

scholarly journals Super-resolution image enhancement by Papoulis-Gerchbergmethod improvement

2010 ◽  
Vol 19 (2) ◽  
pp. 118-123
Author(s):  
Hyo-Sik Jang ◽  
Duk-Gyoo Kim ◽  
Yoon-Soo Jung ◽  
Tae-Gyoun Lee ◽  
Chul-Ho Won
Keyword(s):  
Image Enhancement ◽  
Super Resolution ◽  
Resolution Image

scholarly journals Deep Learning Based Image Enhancement using Super Resolution

2021 ◽  
pp. 190-194
Author(s):  
Nihar Das ◽  
Nisarg Sharma ◽  
Vaishnavi Shebare ◽  
Parth Dawda ◽  
Prajakta Gourkhede ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Input Image ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Research Areas ◽  
Adversarial Network ◽  
Biomedical Microscopy ◽  
Microscopy Images

With the ever-growing field of microscopy there is pretty much a necessity of high - resolution microscopic images. A microscope may have powerful magnifying lenses, but if the resolution is poor, the magnified image is just blur and no useful insights can be gained from such images. Traditional techniques like Structured Illumination Microscopy (SIM) are not feasible enough for proper use and current solutions based on deep learning assume that the input image is noise free. Based on our research and existing applications related to deep learning-based image enhancement, our proposed solution of deep learning based General Adversarial Network (GAN), will help jointly denoise and super-resolved microscopy images. Thus, this project has competitive applications in different research areas including biomedical microscopy, medical diagnosis, astronomical research, surveillance or investigation, etc., and many other areas as well.

scholarly journals The semantic segmentation approach for normal and pathologic tympanic membrane using deep learning

2019 ◽  
Author(s):  
Jungirl Seok ◽  
Jae-Jin Song ◽  
Ja-Won Koo ◽  
Hee Chan Kim ◽  
Byung Yoon Choi
Keyword(s):  
Deep Learning ◽  
Tympanic Membrane ◽  
Medical Records ◽  
Semantic Segmentation ◽  
Detection Accuracy ◽  
Test Set ◽  
Starting Point ◽  
Segmentation Approach ◽  
Validation Set ◽  
Deep Learning Model

AbstractObjectivesThe purpose of this study was to create a deep learning model for the detection and segmentation of major structures of the tympanic membrane.MethodsTotal 920 tympanic endoscopic images had been stored were obtained, retrospectively. We constructed a detection and segmentation model using Mask R-CNN with ResNet-50 backbone targeting three clinically meaningful structures: (1) tympanic membrane (TM); (2) malleus with side of tympanic membrane; and (3) suspected perforation area. The images were randomly divided into three sets – taining set, validation set, and test set – at a ratio of 0.6:0.2:0.2, resulting in 548, 187, and 185 images, respectively. After assignment, 548 tympanic membrane images were augmented 50 times each, reaching 27,400 images.ResultsAt the most optimized point of the model, it achieved a mean average precision of 92.9% on test set. When an intersection over Union (IoU) score of greater than 0.5 was used as the reference point, the tympanic membrane was 100% detectable, the accuracy of side of the tympanic membrane based on the malleus segmentation was 88.6% and detection accuracy of suspicious perforation was 91.4%.ConclusionsAnatomical segmentation may allow the inclusion of an explanation provided by deep learning as part of the results. This method is applicable not only to tympanic endoscope, but also to sinus endoscope, laryngoscope, and stroboscope. Finally, it will be the starting point for the development of automated medical records descriptor of endoscope images.

scholarly journals Convolutional Neural Networks Using Skip Connections with Layer Groups for Super-Resolution Image Reconstruction Based on Deep Learning

2020 ◽  
Vol 10 (6) ◽  
pp. 1959
Author(s):  
Hyeongyeom Ahn ◽  
Changhoon Yim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Convolutional Neural Networks ◽  
Input Data ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Resolution Image ◽  
Hidden Layer ◽  
Layer Groups

In this paper, we propose a deep learning method with convolutional neural networks (CNNs) using skip connections with layer groups for super-resolution image reconstruction. In the proposed method, entire CNN layers for residual data processing are divided into several layer groups, and skip connections with different multiplication factors are applied from input data to these layer groups. With the proposed method, the processed data in hidden layer units tend to be distributed in a wider range. Consequently, the feature information from input data is transmitted to the output more robustly. Experimental results show that the proposed method yields a higher peak signal-to-noise ratio and better subjective quality than existing methods for super-resolution image reconstruction.

scholarly journals SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 236
Author(s):  
Haoran Xu ◽  
Xinya Li ◽  
Kaiyi Zhang ◽  
Yanbai He ◽  
Haoran Fan ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
High Frequency ◽  
Image Inpainting ◽  
Super Resolution ◽  
Low Resolution ◽  
Resolution Image ◽  
Learning Framework ◽  
High Resolution Image ◽  
High Resolution Images

Recently, deep learning has enabled a huge leap forward in image inpainting. However, due to the memory and computational limitation, most existing methods are able to handle only low-resolution inputs, typically less than 1 K. With the improvement of Internet transmission capacity and mobile device cameras, the resolution of image and video sources available to users via the cloud or locally is increasing. For high-resolution images, the common inpainting methods simply upsample the inpainted result of the shrinked image to yield a blurry result. In recent years, there is an urgent need to reconstruct the missing high-frequency information in high-resolution images and generate sharp texture details. Hence, we propose a general deep learning framework for high-resolution image inpainting, which first hallucinates a semantically continuous blurred result using low-resolution inpainting and suppresses computational overhead. Then the sharp high-frequency details with original resolution are reconstructed using super-resolution refinement. Experimentally, our method achieves inspiring inpainting quality on 2K and 4K resolution images, ahead of the state-of-the-art high-resolution inpainting technique. This framework is expected to be popularized for high-resolution image editing tasks on personal computers and mobile devices in the future.

scholarly journals An Improvement of the VDSR Network for Single Image Super-Resolution by Truncation and Adjustment of the Learning Rate Parameters

2019 ◽  
Vol 24 (1) ◽  
pp. 61-68
Author(s):  
Vadim Romanuke
Keyword(s):  
Network Performance ◽  
Super Resolution ◽  
Learning Rate ◽  
Medium Size ◽  
Single Image ◽  
Memory Space ◽  
Resolution Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Bicubic Interpolation

Abstract A problem of single image super-resolution is considered, where the goal is to recover one high-resolution image from one low-resolution image. Whereas this problem has been successfully solved so far by the known VDSR network, such an approach still cannot give an overall beneficial effect compared to bicubic interpolation. This is so due to the fact that the image reconstruction quality has been estimated separately by three subjective factors. Moreover, the original VDSR network consisting of 20 convolutional layers is apparently not optimal by its depth. This is why here those factors are aggregated, and the network performance is deemed by a single estimator. Then the depth is tried to be decreased (truncation) along with adjusting the learning rate drop factor. Finally, a plausible improvement of the VDSR network is confirmed. The best truncated network, performing by almost 3.2 % better than bicubic interpolation, occupies less memory space and is about 1.44 times faster than the original VDSR network for images of a medium size.

Sign in / Sign up

Export Citation Format

Share Document