@inproceedings{
gao2024enhancing,
title={Enhancing Images with Coupled Low-Resolution and Ultra-Dark Degradations: A Tri-level Learning Framework},
author={Jiaxin Gao and Yaohua Liu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=Aupg7lis4d}
}
Due to device constraints and lighting conditions, captured images frequently exhibit coupled low-resolution and ultra-dark  degradations. Enhancing the visibility and resolution of ultra-dark images simultaneously is crucial for practical applications.  Current approaches often address both tasks in isolation or through simplistic cascading strategies, while also relying heavily on empirical and manually designed composite loss constraints, which inevitably results in compromised training efficacy, increased artifacts, and diminished detail fidelity.  To address these issues, we propose TriCo, the first to adopt a Tri-level learning framework that explicitly formulates the bidirectional Cooperative relationship and devises algorithms to tackle coupled degradation factors.  In the optimization across Upper (U)-Middle (M)-Lower (L) levels, we model the synergistic dependencies between illumination learning and  super-resolution tasks  within the M-L levels. Moving to the  U-M levels, we introduce hyper-variables to automate the learning of beneficial constraints for both learning tasks, moving beyond the traditional trial-and-error pitfalls of the learning process. Algorithmically, we establish a Phased Gradient-Response (PGR) algorithm as our training mechanism, which facilitates a dynamic, inter-variable gradient feedback and ensures efficient and rapid convergence.  Moreover, we present the Integrated Hybrid Expert Modulator (IHEM), which merges inherent illumination priors with universal semantic model features to adaptively guide pixel-level high-frequency detail recovery. Extensive experimentation validates the framework's broad generalizability across challenging ultra-dark scenarios, outperforming current state-of-the-art methods across 4 real and synthetic benchmark datasets over 8 metrics (e.g., 5.8\%$\uparrow$ in PSNR, 26.6\%$\uparrow$ in LPIPS, and 13.9\%$\uparrow$ in RMSE).
Enhancing Images with Coupled Low-Resolution and Ultra-Dark Degradations: A Tri-level Learning Framework
Jiaxin Gao Yaohua Liu