activity
20192022
most citedDALE : Dark Region-Aware Low-light Image Enhancement

10 citations · 13 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20223 cited

AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

Furkan Kınlı, Sami Menteş, Barış Özcan +30

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,…

cs.CV2021

Style Transfer with Target Feature Palette and Attention Coloring

Suhyeon Ha, Guisik Kim, Junseok Kwon

Style transfer has attracted a lot of attentions, as it can change a given image into one with splendid artistic styles while preserving the image structure. However, conventional…

eess.IV202010 cited

DALE : Dark Region-Aware Low-light Image Enhancement

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 th…

cs.CV2019

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

Andreas Lugmayr, Martin Danelljan, Radu Timofte +18

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting,…

cs.CV2019

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

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). In this paper, the i…