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1. AR-PPF: Advanced Resolution-Based Pixel Preemption Data Filtering for Efficient Time-Series Data Analysis

Author

Kim, Taewoong, Choi, Kukjin, and Kim, Sungjun

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Databases, Computer Science - Performance
Abstract

With the advent of automation, many manufacturing industries have transitioned to data-centric methodologies, giving rise to an unprecedented influx of data during the manufacturing process. This data has become instrumental in analyzing the quality of manufacturing process and equipment. Engineers and data analysts, in particular, require extensive time-series data for seasonal cycle analysis. However, due to computational resource constraints, they are often limited to querying short-term data multiple times or resorting to the use of summarized data in which key patterns may be overlooked. This study proposes a novel solution to overcome these limitations; the advanced resolution-based pixel preemption data filtering (AR-PPF) algorithm. This technology allows for efficient visualization of time-series charts over long periods while significantly reducing the time required to retrieve data. We also demonstrates how this approach not only enhances the efficiency of data analysis but also ensures that key feature is not lost, thereby providing a more accurate and comprehensive understanding of the data., Comment: 7pages, preprint, '24 Samsung Best Paper Awards

Published
2024

6. AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

Author

Kınlı, Furkan, Menteş, Sami, Özcan, Barış, Kıraç, Furkan, Timofte, Radu, Zuo, Yi, Wang, Zitao, Zhang, Xiaowen, Zhu, Yu, Li, Chenghua, Leng, Cong, Cheng, Jian, Liu, Shuai, Feng, Chaoyu, Bai, Furui, Wang, Xiaotao, Lei, Lei, Ma, Tianzhi, Gao, Zihan, He, Wenxin, Yeo, Woon-Ha, Oh, Wang-Taek, Kim, Young-Il, Ryu, Han-Cheol, He, Gang, Long, Shaoyi, Sharif, S. M. A., Naqvi, Rizwan Ali, Kim, Sungjun, Kim, Guisik, Lee, Seohyeon, Nathan, Sabari, and Kansal, Priya

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. Social media filters transform the images by consecutive non-linear operations, and the feature maps of the original content may be interpolated into a different domain. This reduces the overall performance of the recent deep learning strategies. The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content. The proposed solutions are ranked in terms of the PSNR value with respect to the original images. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge. The comparison of qualitative results of the proposed solutions and the benchmark for the challenge are presented in this report., Comment: 14 pages, 9 figures, Challenge report of AIM 2022 Instagram Filter Removal Challenge in conjunction with ECCV 2022

Published
2022

25. Fat-saturated image generation from multi-contrast MRIs using generative adversarial networks with Bloch equation-based autoencoder regularization

Author

Kim, Sewon, Jang, Hanbyol, Hong, Seokjun, Hong, Yeong Sang, Bae, Won C, Kim, Sungjun, and Hwang, Dosik

Subjects
Biomedical and Clinical Sciences, Engineering, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Magnetic resonance image, Image synthesis, Multi-contrast imaging, Geneartive adversarial networks, Autoencoder regularization, Bloch equation, Medical and Health Sciences, Nuclear Medicine & Medical Imaging, Biomedical and clinical sciences
Abstract

Obtaining multiple series of magnetic resonance (MR) images with different contrasts is useful for accurate diagnosis of human spinal conditions. However, this can be time consuming and a burden on both the patient and the hospital. We propose a Bloch equation-based autoencoder regularization generative adversarial network (BlochGAN) to generate a fat saturation T2-weighted (T2 FS) image from T1-weighted (T1-w) and T2-weighted (T2-w) images of human spine. To achieve this, our approach was to utilize the relationship between the contrasts using Bloch equation since it is a fundamental principle of MR physics and serves as a physical basis of each contrasts. BlochGAN properly generated the target-contrast images using the autoencoder regularization based on the Bloch equation to identify the physical basis of the contrasts. BlochGAN consists of four sub-networks: an encoder, a decoder, a generator, and a discriminator. The encoder extracts features from the multi-contrast input images, and the generator creates target T2 FS images using the features extracted from the encoder. The discriminator assists network learning by providing adversarial loss, and the decoder reconstructs the input multi-contrast images and regularizes the learning process by providing reconstruction loss. The discriminator and the decoder are only used in the training process. Our results demonstrate that BlochGAN achieved quantitatively and qualitatively superior performance compared to conventional medical image synthesis methods in generating spine T2 FS images from T1-w, and T2-w images.

Published
2021

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