arXiv Computer Vision
Multi-exposure HDR Imaging: A Review of Pixel-level and Feature-level Reconstruction Methods
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Sep 23
High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences
Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow detail loss, making high-quality high dynamic ra...
arXiv Computer Vision
Sep 24
High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences
arXiv:2609.27274v1 Announce Type: new Abstract: Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow...
arXiv Computer Vision
Sep 11
InstantHDR: Single-forward Gaussian Splatting Initialization for HDR 3D Reconstruction
InstantHDR is a feed-forward network that initializes high dynamic range (HDR) 3D scenes from uncalibrated multi-exposure low dynamic range (LDR) image collections in a single forward pass. It uses geometry-guided appearance modeling for multi-exposure fusion and a meta-network for scene-specific tone mapping. The authors also created a pre-training dataset, HDR-Pretrain, with 168 Blender-rendered scenes to support generalizable HDR models, achieving a speedup of about 700× over state‑of‑the‑art optimization methods while maintaining comparable quality after lightweight post‑optimization.
arXiv Computer Vision
Sep 4
Generating HDR Video from SDR Video
The paper presents a framework for converting standard dynamic range (SDR) videos into high dynamic range (HDR) videos using large-scale generative video models. It introduces a Multi-Exposure Video Model (MEVM) that predicts exposure-bracketed linear SDR sequences from a single nonlinear SDR input, and a Video Merging Model (VMM) that fuses these predictions into a high-quality HDR sequence while preserving detail in shadows and highlights. Experiments, qualitative evaluation, and a user study demonstrate robust HDR conversion for casual consumer footage and iconic films, and the approach can be integrated into existing SDR generative video pipelines.
arXiv Computer Vision
Sep 28
Double-stream registration with pyramid fusion for HDR video with alternating exposures
The paper introduces a new HDR video reconstruction framework that uses a double-stream registration approach combined with pyramid fusion. It processes three consecutive frames by computing optical flow relative to the central frame and applying a midpoint displacement strategy to address severe overexposure. The resulting radiance and low‑dynamic‑range images are merged in a pyramid fusion stage to produce the final HDR output, and experiments show the method outperforms existing state‑of‑the‑art techniques.
arXiv Computer Vision
4d ago
Prior-Guided Single-Exposure HDR Novel View Synthesis via Base-Gain Representation
PriorGS is a 3D Gaussian Splatting framework for single‑exposure high dynamic range (HDR) novel view synthesis that uses learned priors to guide HDR reconstruction and camera response function (CRF) estimation. It introduces a 3D base‑gain HDR representation, separating HDR radiance into an LDR base and a 3D gain field, which improves robustness to inconsistencies in pseudo‑HDR targets and enables 3D HDR appearance editing. Experiments show PriorGS outperforms existing baselines in HDR reconstruction quality.