Image restoration is essential for enhancing degraded images across computer vision tasks. However, most existing methods address only a single type of degradation (e.g., blur, noise, or haze) at a time, limiting their real-world applicability where multiple degradations often occur simultaneously. In this paper, we propose UniCoRN, a unified image restoration approach capable of handling multiple degradation types simultaneously using a multi-head diffusion model. Specifically, we uncover the potential of low-level visual cues extracted from images in guiding a controllable diffusion model for real-world image restoration and we design a multi-head control network adaptable via a mixture-of-experts strategy. We train our model without any prior assumption of specific degradations, through a smartly designed curriculum learning recipe. Additionally, we also introduce MetaRestore, a metalens imaging benchmark containing images with multiple degradations and artifacts. Extensive evaluations on several challenging datasets, including our benchmark, demonstrate that our method achieves significant performance gains and can robustly restore images with severe degradations. Project page:this https URL
View on arXiv@article{mandal2025_2503.15868,
title={ UniCoRN: Latent Diffusion-based Unified Controllable Image Restoration Network across Multiple Degradations },
author={ Debabrata Mandal and Soumitri Chattopadhyay and Guansen Tong and Praneeth Chakravarthula },
journal={arXiv preprint arXiv:2503.15868},
year={ 2025 }
}