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: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...
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.
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.
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.
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.
arXiv:2604.06161v3 Announce Type: replace-cross Abstract: Most digital videos are stored in 8-bit low dynamic range (LDR) formats, where much of the original high dynamic range (HDR) scene radiance i...
The paper introduces a method for progressively extending the dynamic range of an image by learning to increase it by a single exposure value first, then applying the network recurrently to achieve full HDR reconstruction. The approach is agnostic to input dynamic range, targets a bounded output domain, and utilizes RAW images with adversarial losses to produce realistic results. Memory Replay during backpropagation allows training over multiple inference stages, reducing reconstruction errors and enabling robust recovery of bright highlights in long‑tailed HDR scenes.
arXiv:2604.11788v2 Announce Type: replace Abstract: High dynamic range (HDR) imagery offers a rich and faithful representation of scene radiance, but remains challenging for generative models due to...
We propose DOME-HDR, a dual-output multi-exposure HDR reconstruction framework that jointly produces a perceptually balanced SDR image and a consistent HDR image via gain map inverse tone mapping. Given three bracketed LDR inputs, DOME-HDR first synthesizes a base SDR using a LoRA-adapted latent diffusion model.
arXiv:2611.07665v1 Announce Type: new Abstract: Capturing scenes with extreme motion and high dynamic range is fundamentally constrained by trade-offs among temporal resolution, spatial detail, signa...
HIVE-Diff is an event-conditioned video diffusion framework that reconstructs high‑frame‑rate HDR video from a single low‑dynamic‑range (LDR) reference frame and event data. It separates the task into event‑guided temporal generation and HDR content completion, enabling the creation of multiple HDR frames from one LDR observation. The model is trained on synthetic data and adapted to real events using teacher‑student distillation with LDR‑event sequences as proxy supervision, achieving strong reconstruction quality across synthetic and real datasets.