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Improved Recall of Plant Disease Detection Model using Image Super Resolution
Hyeonggyeong Kim, Chaesung Lim, Seungmin Tak
http://doi.org/10.5626/JOK.2024.51.2.125
Early identification and diagnosis of plant disease is very important because plant diseases have a great impact on yield. Currently, research on developing and advancing models for diagnosing plant diseases and pests using artificial intelligence is being actively conducted. However, even if the model showed good performance during verification, the performance deteriorates when the resolution of the input image is low during operation. If disease control is delayed because of delayed diseases diagnosis due to low resolution, the entire crop is affected by the diseases resulting in a decrease in yield. The purpose of this study was to improve the reproducibility of the model by utilizing super-resolution that increases the resolution of the image. BICUBIC, SRCNN, and SRGAN were used as super-resolution algorithms. After x4 scale super-resolution of test images with 64×64, 128×128, and 192×192 resolutions, they were directly input into the trained YOLOv5 model. As a result, the recall improved by 34% in SRGAN, 30% in SRCNN, and 19% in BICUBIC.
Super Resolution-based Robust Image Inpainting for Large-scale Missing Regions
Jieun Lee, SeungWon Jung, Jonghwa Shim, Eenjun Hwang
http://doi.org/10.5626/JOK.2022.49.9.708
Image inpainting is a method of filling missing regions of an image with plausible imagery. Even though the performance of recent inpainting methods has been significantly improved owing to the introduction of deep learning, unnatural results can be obtained when an input image has a large-scale missing region, contains a complex scene, or is a high-resolution image. In this study, we propose a super resolution-based two-stage image inpainting method, motivated by the point that inpainting performance in low-resolution images is better than in high-resolution images. In the first step, we convert a high-resolution image into a low-resolution image and then perform image inpainting, which results in the initial output image. In the next step, the initial output image becomes the final output image, with the same resolution as the original input image using the super resolution model. To verify the effectiveness of the proposed method, we conducted quantitative and qualitative evaluations using the high-resolution Urban100 dataset. Furthermore, we analyzed the inpainting performance depending on the size of the missing region and demonstrated that the proposed method could generate satisfactory results in a free-form mask.
Improving Super-Resolution GAN Performance through Discriminator using U-Net Structure and Auxiliary Classifier
Dong Min Cheon, Younghwan Jeong, Wonsik Lee, Sounghyouk Wi, Sangjin Nam
http://doi.org/10.5626/JOK.2021.48.11.1194
In this paper, We propose a new super resolution method using a Generative Adversarial Network(GAN). Several super resolution techniques, including interpolation, CNN(Convolutional Neural Network), and GAN, have been proposed. Among them, GAN is the most preferred because of its good performance in image synthesis. Consequently, there have been many attempts to improve the super resolution quality by changing the network structure and loss function of GAN’s Generator, but the focus of improvement was not focused on the discriminator. The findings of the present study confirmed that the U-Net structure and the auxiliary classifier structure for image rotation, which were presented in other papers, had a positive effect on super-resolution network.
Interpolation Method for CT Image Reconstruction using Features Inferred by Self-Supervised Learning
http://doi.org/10.5626/JOK.2021.48.9.1007
Since volumetric data includes internal information, it has an advantage of performing quantitative analysis. Especially medical image data render 3D structures of internal organs, and cubic voxel is necessary for accurate visualization. However, CT image volume is acquired in low z-resolution to reduce X-ray dose exposure. Between slices, image interpolation is a necessary step for visualization as well as for 3D data analysis. In this paper, we propose a self-supervised learning algorithm as an interpolation method that uses the information from the high-resolution images to infer missing information between slices. To achieve this, downscaled slice images are given as the input of the network, and the network recovers the original slice images from the downscaled images. The result of our method outperformed the commonly practiced interpolation methods - nearest-neighbor and trilinear interpolation – in the field, with respect to estimating details. Also, we verified that the proposed algorithm performs comparably with the supervised model with the same network.
Vehicle Image Data Augmentation by GAN-based Viewpoint Transformation
Hangyel Sun, Myeonghee Lee, Charmgil Hong, Injung Kim
http://doi.org/10.5626/JOK.2021.48.8.885
We introduce a novel GAN-based image synthesis method that transforms vehicle images captured from arbitrary viewpoints into images taken from a specific viewpoint. Training a vehicle image recognizer requires a large number of vehicle images taken from a specific viewpoint. However, in practice, it is difficult to collect such training data, especially for newly released vehicles. Therefore, we propose a method of augmenting vehicle image data by converting a vehicle image from an arbitrary viewpoint into an image from a specific viewpoint. The proposed method first transforms a vehicle image from an arbitrary viewpoint to an image taken from the top-front view using DRGAN, then enhances the image quality with DeblurGAN, and finally, improves the resolution using SRGAN. The experimental results demonstrated that the proposed method successfully converted an image taken within 45 degrees left and right into an image from the top-frontal view and was effective in improving the image quality and resolution.
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