REVIEW 5 cited by
HVI: A New Color Space for Low-light Image Enhancement
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable intensity. The former enforces small distances for red coordinates to remove the red artifacts, while the latter compresses the low-light regions to remove the black artifacts. To fully leverage the chromatic and intensity information, a novel Color and Intensity Decoupling Network (CIDNet) is further introduced to learn accurate photometric mapping function under different lighting conditions in the HVI space. Comprehensive results from benchmark and ablation experiments show that the proposed HVI color space with CIDNet outperforms the state-of-the-art methods on 10 datasets. The code is available at https://github.com/Fediory/HVI-CIDNet.
Forward citations
Cited by 5 Pith papers
-
Unsupervised Ultra-High-Resolution UAV Low-Light Image Enhancement: A Benchmark, Metric and Framework
A new 4K UAV low-light image enhancement dataset, an efficiency-aware metric (EEI), and a lightweight unsupervised framework (U3LIE) that achieves 23.8 FPS at 4K.
-
Degradation-Consistent Learning via Bidirectional Diffusion for Low-Light Image Enhancement
Training a diffusion model on both enhancement and degradation paths, with a shared encoder and a reflection-aware correction module, yields state-of-the-art low-light enhancement on multiple benchmarks.
-
From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning
An unsupervised diffusion-based enhancer with caption, reflectance, and cycle-attention consistency losses improves zero-shot classification, face detection, and night segmentation on low-light images.
-
HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement
HVI-CIDNet+ replaces the HSV color plane with polarized hue-saturation coordinates and a learned dark-intensity collapse, then trains a dual-branch transformer-CNN network with CLIP-derived priors for low-light enhancement.
-
NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results
In the first NTIRE 2025 video conferencing quality enhancement challenge, look-up-table based methods won a crowdsourced human ranking over ten submissions.
Discussion (0). Sign in to comment.