3 papers
cs.CV2026
Learning Structural Illumination for Unsupervised Low-light Enhancement
Tianle Du, Peiyuan He, Hainuo Wang +2
Existing unsupervised low-light image enhancement (LLIE) methods often estimate illumination directly from the entire low-light input, without separating its spatially varying illu…
cs.CV2026
On the Global Photometric Alignment for Low-Level Vision
Mingjia Li, Tianle Du, Hainuo Wang +2
Supervised low-level vision models rely on pixel-wise losses against paired references, yet paired training sets exhibit per-pair photometric inconsistency, say, different image pa…
cs.CV2026
Anchor then Polish for Low-light Enhancement
Tianle Du, Mingjia Li, Hainuo Wang +1
Low-light image enhancement is challenging due to entangled degradations, mainly including poor illumination, color shifts, and texture interference. Existing methods often rely on…