Author name cluster

Jihyong Oh

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

AAAI Conference 2026 Conference Paper

I-INR: Iterative Implicit Neural Representations

Ali Haider
Muhammad Salman Ali
Maryam Qamar
Tahir Khalil
Soo Ye Kim
Jihyong Oh
Enzo Tartaglione
Sung-Ho Bae

Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable functions parameterized by neural networks. However, INRs are prone to the spectral bias problem, limiting their ability to retain high-frequency information, and often struggle with noise robustness. Motivated by recent trends in iterative refinement processes, we propose Iterative Implicit Neural Representations (I-INRs). This novel plug-and-play framework iteratively refines signal reconstructions to restore high-frequency details, improve noise robustness, and enhance generalization, ultimately delivering superior reconstruction quality. I-INRs integrate seamlessly into existing INR architectures with only a 0.5–2% increase in parameters. During reconstruction, the iterative refinement adds just 0.8–1.6% additional FLOPs over the baseline while delivering a substantial performance boost of up to +2.0 PSNR. Extensive experiments demonstrate that I-INRs consistently outperform WIRE, SIREN, and Gauss across various computer vision tasks, including image fitting, image denoising, and object occupancy prediction.

PDF Details DOI

AAAI Conference 2026 Conference Paper

MoBGS: Motion Deblurring Dynamic 3D Gaussian Splatting for Blurry Monocular Video

Minh-Quan Viet Bui
Jongmin Park
Juan Luis Gonzalez
Jaeho Moon
Jihyong Oh
Munchurl Kim

We present MoBGS, a novel motion deblurring 3D Gaussian Splatting (3DGS) framework capable of reconstructing sharp and high-quality novel spatio-temporal views from blurry monocular videos in an end-to-end manner. Existing dynamic novel view synthesis (NVS) methods are highly sensitive to motion blur in casually captured videos, resulting in significant degradation of rendering quality. While recent approaches address motion-blurred inputs for NVS, they primarily focus on static scene reconstruction and lack dedicated motion modeling for dynamic objects. To overcome these limitations, our MoBGS introduces a novel Blur-adaptive Latent Camera Estimation (BLCE) method using a proposed Blur-adaptive Neural Ordinary Differential Equation (ODE) solver for effective latent camera trajectory estimation, improving global camera motion deblurring. In addition, we propose a Latent Camera-induced Exposure Estimation (LCEE) method to ensure consistent deblurring of both a global camera and local object motions. Extensive experiments on the Stereo Blur dataset and real-world blurry videos show that our MoBGS significantly outperforms the very recent methods, achieving state-of-the-art performance for dynamic NVS under motion blur.

PDF Details DOI

AAAI Conference 2020 Conference Paper

FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss

Soo Ye Kim
Jihyong Oh
Munchurl Kim

Super-resolution (SR) has been widely used to convert lowresolution legacy videos to high-resolution (HR) ones, to suit the increasing resolution of displays (e. g. UHD TVs). However, it becomes easier for humans to notice motion artifacts (e. g. motion judder) in HR videos being rendered on largersized display devices. Thus, broadcasting standards support higher frame rates for UHD (Ultra High Deﬁnition) videos (4K@60 fps, 8K@120 fps), meaning that applying SR only is insufﬁcient to produce genuine high quality videos. Hence, to up-convert legacy videos for realistic applications, not only SR but also video frame interpolation (VFI) is necessitated. In this paper, we ﬁrst propose a joint VFI-SR framework for upscaling the spatio-temporal resolution of videos from 2K 30 fps to 4K 60 fps. For this, we propose a novel training scheme with a multi-scale temporal loss that imposes temporal regularization on the input video sequence, which can be applied to any general video-related task. The proposed structure is analyzed in depth with extensive experiments.

PDF Details

AAAI Conference 2020 Conference Paper

JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video

Soo Ye Kim
Jihyong Oh
Munchurl Kim

Joint learning of super-resolution (SR) and inverse tonemapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications. However, previous CNN-based methods directly reconstruct the HR HDR frames from LR SDR frames, and are only trained with a simple L2 loss. In this paper, we take a divide-and-conquer approach in designing a novel GANbased joint SR-ITM network, called JSI-GAN, which is composed of three task-speciﬁc subnets: an image reconstruction subnet, a detail restoration (DR) subnet and a local contrast enhancement (LCE) subnet. We delicately design these subnets so that they are appropriately trained for the intended purpose, learning a pair of pixel-wise 1D separable ﬁlters via the DR subnet for detail restoration and a pixel-wise 2D local ﬁlter by the LCE subnet for contrast enhancement. Moreover, to train the JSI-GAN effectively, we propose a novel detail GAN loss alongside the conventional GAN loss, which helps enhancing both local details and contrasts to reconstruct high quality HR HDR results. When all subnets are jointly trained well, the predicted HR HDR results of higher quality are obtained with at least 0. 41 dB gain in PSNR over those generated by the previous methods. The ofﬁcial Tensorﬂow code is available at https: //github. com/JihyongOh/JSI-GAN.

PDF Details