5 papers
Linear Recurrent Unit with Semantic Modulation for Image Super-Resolution
Mingyu Choi, Woo Kyoung Han, Sunghoon Im +1
Linear recurrent unit (LRU), designed with a principled formulation for stable linear recurrence, has demonstrated promising accuracy and robustness on long-range dependency tasks.…
JPEG Processing Neural Operator for Backward-Compatible Coding
Woo Kyoung Han, Yongjun Lee, Byeonghun Lee +3
Despite significant advances in learning-based lossy compression algorithms, standardizing codecs remains a critical challenge. In this paper, we present the JPEG Processing Neural…
Towards Lossless Implicit Neural Representation via Bit Plane Decomposition
Woo Kyoung Han, Byeonghun Lee, Hyunmin Cho +2
We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as t…
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow
EungGu Kang, Byeonghun Lee, Sunghoon Im +1
Multi frame super-resolution(MFSR) achieves higher performance than single image super-resolution (SISR), because MFSR leverages abundant information from multiple frames. Recent M…
JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients
Woo Kyoung Han, Sunghoon Im, Jaedeok Kim +1
We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation. The JPEG algorithm significantly quanti…