23 citations · 23 across the 3 of their papers we have counts for
6 papers
PMQ-VE: Progressive Multi-Frame Quantization for Video Enhancement
ZhanFeng Feng, Long Peng, Xin Di +7
Multi-frame video enhancement tasks aim to improve the spatial and temporal resolution and quality of video sequences by leveraging temporal information from multiple frames, which…
Brightness Perceiving for Recursive Low-Light Image Enhancement
Haodian Wang, Long Peng, Yuejin Sun +3
Due to the wide dynamic range in real low-light scenes, there will be large differences in the degree of contrast degradation and detail blurring of captured images, making it diff…
Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling
Long Peng, Anran Wu, Wenbo Li +9
Arbitrary-scale super-resolution (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors using a single model, addr…
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration
Long Peng, Xin Di, Zhanfeng Feng +6
Image restoration aims to recover details and enhance contrast in degraded images. With the growing demand for high-quality imaging (\textit{e.g.}, 4K and 8K), achieving a balance…
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution
Long Peng, Wenbo Li, Jiaming Guo +7
Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods…
Mamba: Arbitrary-Scale Super-Resolution via Scaleable State Space Model
Peizhe Xia, Long Peng, Xin Di +4
Arbitrary scale super-resolution (ASSR) aims to super-resolve low-resolution images to high-resolution images at any scale using a single model, addressing the limitations of tradi…