We are delighted to announce that two papers from the Creative Vision and Multimedia (CM Lab, Prof. Jihyong Oh) has been accepted to Neurocomputing.

Title:
SoFoNO: Arbitrary-scale image super-resolution via Sobolev Fourier neural operator

Authors:
Jong Kwon Oh, Hwijae Son, Hyung Ju Hwang, Jihyong Oh

Abstract:
Accurately reconstructing fine textures and sharp edges remains a significant challenge in Single Image Super-Resolution (SISR) tasks, often resulting in overly smooth and less realistic images. To alleviate this issue we propose a novel SISR framework named Sobolev Fourier Neural Operator (SoFoNO). Central to our approach is a specialized architecture featuring a Sobolev Branch, which effectively captures detailed structures in the frequency domain via a learnable Sobolev exponent. Importantly, the learned Sobolev exponent is directly employed as derivative order parameters within the Sobolev loss function, enabling more precise and visually coherent reconstructions. Unlike conventional pixel-level loss functions, the Sobolev loss explicitly incorporates frequency-domain penalties, significantly enhancing the reconstruction quality of detailed image structures. Extensive experiments conducted on multiple datasets under both in-scale and out-scale scenarios demonstrate that our SoFoNO provides robust and effective performance in arbitrary-scale super-resolution, consistently outperforming representative existing methods across various tested scale factors without relying on attention mechanisms.

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Title:
MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing

Authors:
Jeahun Sung, Changhyun Roh, Chanho Eom, Jihyong Oh

Abstract:
Recent advances in portable imaging have made camera-based screen capture ubiquitous. Unfortunately, frequency aliasing between the camera's color filter array (CFA) and the display's sub-pixels induces moiré patterns that severely degrade captured photos and videos. Although various demoiréing models have been proposed to remove such moiré patterns, these approaches still suffer from several limitations: (i) spatially varying artifact strength within a frame, (ii) large-scale and globally spreading structures, (iii) channel-dependent statistics and (iv) rapid temporal fluctuations across frames. We address these issues with the Moiré Conditioned Hybrid Adaptive Transformer (MoCHA-former), which comprises two key components: Decoupled Moiré Adaptive Demoiréing (DMAD) and Spatio-Temporal Adaptive Demoiréing (STAD). DMAD separates moiré and content via a Moiré Decoupling Block (MDB) and a Detail Decoupling Block (DDB), then produces moiré-adaptive features using a Moiré Conditioning Block (MCB) for targeted restoration. STAD introduces a Spatial Fusion Block (SFB) with window attention to capture large-scale structures, and a Feature Channel Attention (FCA) to model channel dependence in RAW frames. To ensure temporal consistency, MoCHA-former performs implicit frame alignment without any explicit alignment module. We analyze moiré characteristics through qualitative and quantitative studies, and evaluate on two video datasets covering RAW and sRGB domains. MoCHA-former consistently surpasses prior methods across PSNR, SSIM, and LPIPS.