Capturing Stable HDR Videos Using a Dual-Camera System

• URL: http://arxiv.org/abs/2507.06593v1
• Date: Wed, 09 Jul 2025 07:09:42 GMT
• Title: Capturing Stable HDR Videos Using a Dual-Camera System
• Authors: Qianyu Zhang, Bolun Zheng, Hangjia Pan, Lingyu Zhu, Zunjie Zhu, Zongpeng Li, Shiqi Wang,
• Abstract summary: In HDR video reconstruction, exposure fluctuations in reference images from alternating exposure methods often result in flickering.<n>We propose a dual-camera system (DCS) for HDR video acquisition, where one camera is assigned to capture consistent reference sequences.<n>We introduce an exposure-adaptive fusion network (EAFNet) to achieve more robust results.
• Score: 19.393435127495668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In HDR video reconstruction, exposure fluctuations in reference images from alternating exposure methods often result in flickering. To address this issue, we propose a dual-camera system (DCS) for HDR video acquisition, where one camera is assigned to capture consistent reference sequences, while the other is assigned to capture non-reference sequences for information supplementation. To tackle the challenges posed by video data, we introduce an exposure-adaptive fusion network (EAFNet) to achieve more robust results. EAFNet introduced a pre-alignment subnetwork to explore the influence of exposure, selectively emphasizing the valuable features across different exposure levels. Then, the enhanced features are fused by the asymmetric cross-feature fusion subnetwork, which explores reference-dominated attention maps to improve image fusion by aligning cross-scale features and performing cross-feature fusion. Finally, the reconstruction subnetwork adopts a DWT-based multiscale architecture to reduce ghosting artifacts and refine features at different resolutions. Extensive experimental evaluations demonstrate that the proposed method achieves state-of-the-art performance on different datasets, validating the great potential of the DCS in HDR video reconstruction. The codes and data captured by DCS will be available at https://github.com/zqqqyu/DCS.

Related papers

• Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation [54.64335350932855]
  We propose a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging. Our proposed Asyn system integrates the Dynamic Vision Sensors (DVS) with a set of LCD panels. The LCD panels modulate the irradiance incident upon the DVS by altering their transparency, thereby triggering the pixel-independent event streams.
  arXiv  Detail & Related papers  (2024-03-14T13:45:09Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model [59.08821399652483]
  Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among these algorithms, diffusion model (DM)-based methods have shown promising performance but are often burdened by heavy computational demands and pixel misalignment issues when predicting the image-level distribution. We propose to leverage DM within a compact latent space to generate concise guidance priors and introduce a novel solution called Reti-Diff for the IDIR task. Reti-Diff comprises two key components: the Retinex-based latent DM (RLDM) and the Retinex-guided transformer (RG
  arXiv  Detail & Related papers  (2023-11-20T09:55:06Z)
• Searching a Compact Architecture for Robust Multi-Exposure Image Fusion [55.37210629454589]
  Two major stumbling blocks hinder the development, including pixel misalignment and inefficient inference. This study introduces an architecture search-based paradigm incorporating self-alignment and detail repletion modules for robust multi-exposure image fusion. The proposed method outperforms various competitive schemes, achieving a noteworthy 3.19% improvement in PSNR for general scenarios and an impressive 23.5% enhancement in misaligned scenarios.
  arXiv  Detail & Related papers  (2023-05-20T17:01:52Z)
• A Unified HDR Imaging Method with Pixel and Patch Level [41.14378863436963]
  We propose a hybrid HDR deghosting network, called HyNet, to generate visually pleasing HDR images. Experiments demonstrate that HyNet outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors.
  arXiv  Detail & Related papers  (2023-04-14T06:21:57Z)
• Ghost-free High Dynamic Range Imaging via Hybrid CNN-Transformer and Structure Tensor [12.167049432063132]
  We present a hybrid model consisting of a convolutional encoder and a Transformer decoder to generate ghost-free HDR images. In the encoder, a context aggregation network and non-local attention block are adopted to optimize multi-scale features. The decoder based on Swin Transformer is utilized to improve the reconstruction capability of the proposed model.
  arXiv  Detail & Related papers  (2022-12-01T15:43:32Z)
• H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System [39.95458608416292]
  High-resolution stereoscopic (H2-Stereo) video allows us to perceive dynamic 3D content fine. Existing methods provide compromised solutions that lack temporal or spatial details. We propose a dual camera system, in which one captures high-spatial-resolution low-frame-rate (HSR-LFR) videos with rich spatial details. We then devise a Learned Information Fusion network (LIFnet) that exploits the cross-camera redundancies to reconstruct the H2-Stereo video effectively.
  arXiv  Detail & Related papers  (2022-08-04T04:06:01Z)
• SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes [17.867412310873732]
  Ghosting artifacts, motion blur, and low fidelity in highlight are the main challenges in High Dynamic Range (LDR) imaging. We propose a joint HDR and denoising pipeline, containing two sub-networks. We create the first joint HDR and denoising benchmark dataset.
  arXiv  Detail & Related papers  (2022-06-20T07:49:56Z)
• MC-Blur: A Comprehensive Benchmark for Image Deblurring [127.6301230023318]
  In most real-world images, blur is caused by different factors, e.g., motion and defocus. We construct a new large-scale multi-cause image deblurring dataset (called MC-Blur) Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios.
  arXiv  Detail & Related papers  (2021-12-01T02:10:42Z)
• A Two-stage Deep Network for High Dynamic Range Image Reconstruction [0.883717274344425]
  This study tackles the challenges of single-shot LDR to HDR mapping by proposing a novel two-stage deep network. Notably, our proposed method aims to reconstruct an HDR image without knowing hardware information, including camera response function (CRF) and exposure settings.
  arXiv  Detail & Related papers  (2021-04-19T15:19:17Z)
• High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion [84.24973877109181]
  We propose a novel attention-based hierarchical multi-modal fusion network for guided DSR. We show that our approach outperforms state-of-the-art methods in terms of reconstruction accuracy, running speed and memory efficiency.
  arXiv  Detail & Related papers  (2021-04-04T03:28:33Z)
• Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline [100.5353614588565]
  We propose to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-02T17:59:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.