PixIE: Prompted Pixel-Space Low-Light Image Enhancement

Abstract

Low-light images suffer from severe noise, contrast loss, and semantic ambiguity, making enhancement a joint problem of denoising and detail recovery. We propose PixIE, a feed-forward pixel-space LLIE framework semantically prompted by a vision foundation model. PixIE first performs cross-scale denoising to suppress noise and preserve structure, then refines details using DINO-Prompted Pixel Blocks (DPPBs), which inject intermediate DINOv3 features through patch-conditioned, spatially continuous per-pixel modulation. To make pixel-space attention efficient across scales, we introduce Spatial-Channel Compaction (SCC), which jointly reduces the spatial token grid and channel dimension. We further propose Multi-Receptive-Field Pixel Embedding (MRPE) to provide neighborhood-aware pixel representations before semantic prompting, improving robustness to signal-dependent noise beyond point-wise embeddings. Experiments on LLIE benchmarks show that PixIE improves average PSNR by 1.9-15.0% over recent state-of-the-art methods and reduces LPIPS by 8.5-44.4%. Qualitative comparisons further show sharper details and more stable textures, improving both reconstruction fidelity and perceptual quality.