{"ID":2858018,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.07961","arxiv_id":"2510.07961","title":"Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement","abstract":"Ultra-High Definition (UHD) image restoration faces a trade-off between computational efficiency and high-frequency detail retention. While Variational Autoencoders (VAEs) improve efficiency via latent-space processing, their Gaussian constraint often discards degradation-specific high-frequency information, hurting reconstruction fidelity. To overcome this, we propose Latent Harmony, a two-stage framework that redefines VAEs for UHD restoration by jointly regularizing the latent space and enforcing high-frequency-aware reconstruction.In Stage One, we introduce LH-VAE, which enhances semantic robustness through visual semantic constraints and progressive degradation perturbations, while latent equivariance strengthens high-frequency reconstruction.Stage Two jointly trains this refined VAE with a restoration model using High-Frequency Low-Rank Adaptation (HF-LoRA): an encoder LoRA guided by a fidelity-oriented high-frequency alignment loss to recover authentic details, and a decoder LoRA driven by a perception-oriented loss to synthesize realistic textures. Both LoRA modules are trained via alternating optimization with selective gradient propagation to preserve the pretrained latent structure.At inference, a tunable parameter α enables flexible fidelity-perception trade-offs.Experiments show Latent Harmony achieves state-of-the-art performance across UHD and standard-resolution tasks, effectively balancing efficiency, perceptual quality, and reconstruction accuracy.","short_abstract":"Ultra-High Definition (UHD) image restoration faces a trade-off between computational efficiency and high-frequency detail retention. While Variational Autoencoders (VAEs) improve efficiency via latent-space processing, their Gaussian constraint often discards degradation-specific high-frequency information, hurting re...","url_abs":"https://arxiv.org/abs/2510.07961","url_pdf":"https://arxiv.org/pdf/2510.07961v3","authors":"[\"Yidi Liu\",\"Xueyang Fu\",\"Jie Huang\",\"Jie Xiao\",\"Dong Li\",\"Wenlong Zhang\",\"Lei Bai\",\"Zheng-Jun Zha\"]","published":"2025-10-09T08:54:26Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"LoRA\",\"Variational Autoencoder\"]","has_code":false}