arXiv:2510.07961v3 Announce Type: replace 
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 {\alpha} 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.

إطار جديد يسمى Latent Harmony على وشك إحداث ثورة في استعادة الصور عالية الدقة (UHD) من خلال معالجة التوازن الشائع بين الكفاءة الحاسوبية والاحتفاظ بالتفاصيل. غالبًا ما تفقد الطرق التقليدية التي تستخدم مشفرات متغيرة (VAEs) معلومات مهمة ذات تردد عالٍ بسبب قيودها الغاوسية. يقدم Latent Harmony نهجًا من مرحلتين يعزز من قدرات VAEs، مما يضمن دقة أفضل في إعادة البناء. هذه الخطوة مهمة لأنها تعد بتحسين جودة الصور UHD، وهو أمر حاسم لمجموعة متنوعة من التطبيقات في وسائل الإعلام والتكنولوجيا.

Un nuevo marco llamado Latent Harmony está a punto de revolucionar la restauración de imágenes en Ultra Alta Definición (UHD) al abordar el compromiso común entre la eficiencia computacional y la retención de detalles. Los métodos tradicionales que utilizan Autoencoders Variacionales (VAEs) a menudo pierden información importante de alta frecuencia debido a sus restricciones gaussianas. Latent Harmony introduce un enfoque en dos etapas que mejora las capacidades de los VAEs, asegurando una mejor fidelidad en la reconstrucción. Este avance es significativo ya que promete mejorar la calidad de las imágenes UHD, lo cual es crucial para diversas aplicaciones en medios y tecnología.

Un nouveau cadre appelé Latent Harmony est sur le point de révolutionner la restauration d'images en Ultra Haute Définition (UHD) en abordant le compromis courant entre l'efficacité computationnelle et la rétention des détails. Les méthodes traditionnelles utilisant des autoencodeurs variationnels (VAEs) perdent souvent des informations importantes à haute fréquence en raison de leurs contraintes gaussiennes. Latent Harmony introduit une approche en deux étapes qui améliore les capacités des VAEs, garantissant une meilleure fidélité de reconstruction. Cette avancée est significative car elle promet d'améliorer la qualité des images UHD, ce qui est crucial pour diverses applications dans les médias et la technologie.

A new framework called Latent Harmony is set to revolutionize Ultra-High Definition (UHD) image restoration by addressing the common trade-off between computational efficiency and detail retention. Traditional methods using Variational Autoencoders (VAEs) often lose important high-frequency information due to their Gaussian constraints. Latent Harmony introduces a two-stage approach that enhances the capabilities of VAEs, ensuring better reconstruction fidelity. This advancement is significant as it promises to improve the quality of UHD images, which is crucial for various applications in media and technology.

Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement

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