[Submitted on 21 Sep 2026]
Title:MirrorDistill: Illumination-Aware Latent Distillation for Efficient Low-Light Restoration
View a PDF of the paper titled MirrorDistill: Illumination-Aware Latent Distillation for Efficient Low-Light Restoration, by Farida Mohsen and 5 other authors
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Abstract:Low-light image enhancement (LLIE) is an im- portant component of visual sensing systems operating under degraded illumination, including nighttime surveillance, au- tonomous navigation, remote sensing, and inspection in poorly lit industrial environments. Most LLIE methods rely on output- level reconstruction losses that supervise only the final restored image, leaving the intermediate feature recovery process weakly constrained. This paper proposes MirrorDistill, an illumination- aware latent distillation framework that links the low-light and clean domains through feature mirroring. During training, a shared encoder and an exponential-moving-average teacher decoder process the clean reference image to generate clean- domain latent targets. These targets supervise the low-light student at two levels: raw encoder features and standardized multi-scale decoder projections. The alignment is applied layer by layer, while a proposed illumination-aware weighting scheme gives greater emphasis to underexposed regions. The teacher and reference branches are used only during training, so inference requires only the lightweight student encoder-decoder and in- troduces no teacher-side computational cost. Under evaluation on the standard LOL benchmarks, MirrorDistill outperforms the state-of-the-art methods on the real-captured LOL-v2-Real set, while having the lowest compute complexity (GMACs) and while remaining competitive on the LOL-v1 and LOL-v2-Synthetic datasets. Ablation studies further show the contributions of the encoder mirror, decoder mirror, and illumination-aware weighting. Finally, we release our code as open-source for the benefit of future research.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.25331 [cs.CV]
(or arXiv:2609.25331v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.25331
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Farida Mohsen [view email] [v1] Mon, 21 Sep 2026 19:20:02 UTC (18,965 KB)
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