arXiv:2608. 00111v1 Announce Type: cross Abstract: Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference.
By Kuan-Yen Chen, Fang-Yi Su, Philip Chikontwe, Jung-Hsien Chiang
arXiv:2608.30129v1 Announce Type: new
Abstract: This work presents $\textbf{Lapis}$, a $\textbf{l}$inear-$\textbf{a}$ttention-based $\textbf{pi}$xel-$\textbf{s}$pace generative framework that achieve...
By Bingde Liu, Wu Ran, Jinglei Zhang, Huanhuan Yuan, Chao Ma
arXiv:2604.24136v3 Announce Type: replace
Abstract: Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohi...
By Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu, Chia-Hung Yuan, Wei-Chen Chiu, Ching-Chun Huang
arXiv:2509. 05441v4 Announce Type: replace-cross Abstract: Latent generative models compress images into learned embeddings prior to synthesis, and the generation quality critically depends on how faithfully these embeddings preserve visual detail.
By Tejaswini Medi, Hsien-Yi Wang, Arianna Rampini, Margret Keuper
arXiv:2608. 10544v1 Announce Type: cross Abstract: Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations.
By Sangwoo Jo, Donggeun Ko, Jayeon Kang, Youngsang Kwak, Jaehwa Kwak, Sungjoon Choi
GraftSR is a diffusion-based super‑resolution framework that uses reference images of the same object to guide texture restoration, mitigating hallucination. It introduces a dual‑mask reference guidance mechanism to decouple texture extraction from application, avoiding reliance on spatial alignment. The authors also release TexRefSR‑141K, a large dataset of reference pairs with spatial masks, and show that GraftSR outperforms existing methods on the TexRefSR‑Eval benchmark, reducing LPIPS by 20.2%.
By Qifan Yu, Haoran Bai, Zongyao He, Weijie He, Sibin Deng, Honggang Qi, Ying Chen