AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

1 code implementation • 17 Oct 2022 • Furkan Kınlı, Sami Menteş, Barış Özcan, Furkan Kıraç, Radu Timofte, Yi Zuo, Zitao Wang, Xiaowen Zhang, Yu Zhu, Chenghua Li, Cong Leng, Jian Cheng, Shuai Liu, Chaoyu Feng, Furui Bai, Xiaotao Wang, Lei Lei, Tianzhi Ma, Zihan Gao, Wenxin He, Woon-Ha Yeo, Wang-Taek Oh, Young-Il Kim, Han-Cheol Ryu, Gang He, Shaoyi Long, S. M. A. Sharif, Rizwan Ali Naqvi, Sungjun Kim, Guisik Kim, Seohyeon Lee, Sabari Nathan, Priya Kansal

This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal.

Style Transfer with Target Feature Palette and Attention Coloring

no code implementations • 7 Nov 2021 • Suhyeon Ha, Guisik Kim, Junseok Kwon

In this paper, to solve these problems, a novel artistic stylization method with target feature palettes is proposed, which can transfer key features accurately.

Style Transfer

DALE : Dark Region-Aware Low-light Image Enhancement

1 code implementation • 28 Aug 2020 • Dokyeong Kwon, Guisik Kim, Junseok Kwon

In this paper, we present a novel low-light image enhancement method called dark region-aware low-light image enhancement (DALE), where dark regions are accurately recognized by the proposed visual attention module and their brightness are intensively enhanced.

Low-Light Image Enhancement

AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results

1 code implementation • 18 Nov 2019 • Andreas Lugmayr, Martin Danelljan, Radu Timofte, Manuel Fritsche, Shuhang Gu, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan, Nam Hyung Joon, Yu Seung Won, Guisik Kim, Dokyeong Kwon, Chih-Chung Hsu, Chia-Hsiang Lin, Yuanfei Huang, Xiaopeng Sun, Wen Lu, Jie Li, Xinbo Gao, Sefi Bell-Kligler

For training, only one set of source input images is therefore provided in the challenge.

Image Super-Resolution

LED2Net: Deep Illumination-aware Dehazing with Low-light and Detail Enhancement

no code implementations • 12 Jun 2019 • Guisik Kim, Junseok Kwon

We present a novel dehazing and low-light enhancement method based on an illumination map that is accurately estimated by a convolutional neural network (CNN).