Single-shot HDR using conventional image sensor shutter functions and optical randomization

2025-06-27

AI Summary

Traditional HDR imaging suffers from motion artifacts due to multi-exposure acquisition, while single-exposure methods exhibit insufficient reconstruction fidelity in highly saturated highlight regions. To address this, we propose a novel single-shot HDR imaging paradigm integrating Global Reset Release (GRR) shuttering with optical randomization. Specifically, we pioneer the combination of commodity sensor GRR operation and fiber-bundle–based spatial image scrambling to introduce exposure diversity, thereby enhancing information recoverability in saturated regions. We further develop an optimization-based reconstruction algorithm incorporating total variation regularization, enabling HDR recovery from a single 8-bit frame. Experiments demonstrate that our method significantly outperforms existing single-shot HDR approaches under >10% pixel saturation, and matches their performance under mild (1%) saturation. A prototype system employing a 48 dB 8-bit sensor achieves a 73 dB dynamic range, effectively overcoming the fundamental bottleneck of single-frame HDR reconstruction under large-area saturation.

Technology Category

• Computer Vision: Multi-modal Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

High-dynamic-range (HDR) imaging is an essential technique for overcoming the dynamic range limits of image sensors. The classic method relies on multiple exposures, which slows capture time, resulting in motion artifacts when imaging dynamic scenes. Single-shot HDR imaging alleviates this issue by encoding HDR data into a single exposure, then computationally recovering it. Many established methods use strong image priors to recover improperly exposed image detail. These approaches struggle with extended highlight regions. We utilize the global reset release (GRR) shutter mode of an off-the-shelf sensor. GRR shutter mode applies a longer exposure time to rows closer to the bottom of the sensor. We use optics that relay a randomly permuted (shuffled) image onto the sensor, effectively creating spatially randomized exposures across the scene. The exposure diversity allows us to recover HDR data by solving an optimization problem with a simple total variation image prior. In simulation, we demonstrate that our method outperforms other single-shot methods when many sensor pixels are saturated (10% or more), and is competitive at a modest saturation (1%). Finally, we demonstrate a physical lab prototype that uses an off-the-shelf random fiber bundle for the optical shuffling. The fiber bundle is coupled to a low-cost commercial sensor operating in GRR shutter mode. Our prototype achieves a dynamic range of up to 73dB using an 8-bit sensor with 48dB dynamic range.

Problem

• Overcoming dynamic range limits in single-shot HDR imaging
• Reducing motion artifacts in dynamic scene HDR capture
• Recovering HDR data from saturated sensor pixels effectively

Innovation

• Uses GRR shutter mode for varied exposure times
• Employs optical randomization to shuffle image
• Solves optimization with total variation prior