Attention-guided Network for Ghost-free High Dynamic Range Imaging (1904.10293v1)

Abstract: Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes. Previous methods first register the input low dynamic range (LDR) images using optical flow before merging them, which are error-prone and cause ghosts in results. A very recent work tries to bypass optical flows via a deep network with skip-connections, however, which still suffers from ghosting artifacts for severe movement. To avoid the ghosting from the source, we propose a novel attention-guided end-to-end deep neural network (AHDRNet) to produce high-quality ghost-free HDR images. Unlike previous methods directly stacking the LDR images or features for merging, we use attention modules to guide the merging according to the reference image. The attention modules automatically suppress undesired components caused by misalignments and saturation and enhance desirable fine details in the non-reference images. In addition to the attention model, we use dilated residual dense block (DRDB) to make full use of the hierarchical features and increase the receptive field for hallucinating the missing details. The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error. Experiments on different datasets show that the proposed AHDRNet can achieve state-of-the-art quantitative and qualitative results.

• The paper introduces AHDRNet, an attention-guided network that fuses multi-exposure images to effectively eliminate ghost artifacts.
• It employs dilated residual dense blocks to enlarge the receptive field and recover details without relying on optical flow.
• Experimental results show superior PSNR and HDR-VDP-2 metrics, demonstrating a significant advancement in HDR imaging quality.

Attention-guided Network for Ghost-free High Dynamic Range Imaging: An In-depth Analysis

This essay elucidates the paper titled "Attention-guided Network for Ghost-free High Dynamic Range Imaging," which introduces an innovative approach to High Dynamic Range (HDR) imaging. This research is dedicated to ameliorating ghosting artifacts in HDR images caused by misalignments and movement in dynamic scenes, a prevalent challenge in HDR imaging.

The primary contribution of this research is the introduction of a novel attention-guided deep neural network, known as AHDRNet, which aims to generate ghost-free HDR images. Unlike traditional techniques that rely on optical flow for image alignment—a method prone to errors, leading to ghosting—the proposed AHDRNet eschews optical flow, instead utilizing attention mechanisms and hierarchical feature extraction to improve image coherence and detail retention.

Technical Contributions

1. Attention Mechanism for Feature Fusion: The AHDRNet leverages an attention mechanism to selectively focus on and enhance features from multiple input Low Dynamic Range (LDR) images. This module evaluates the importance of each image region, automatically suppressing artifacts induced by movement or saturation. The attention maps guide the fusion process, mitigating early-stage misalignments and effectively reducing ghosting.
2. Dilated Residual Dense Blocks (DRDB): To address the challenge of hallucinating details in regions affected by saturation and movement, the network employs Dilated Residual Dense Blocks. These blocks enlarge the receptive field, thereby allowing the network to contextually interpolate missing details without relying on optical flow-based registration. This enhances the capability of the network in capturing extensive image details from LDR inputs.
3. Ghost-free HDR Synthesis: By stacking non-reference image features and referencing image features with attention applied guidance, AHDRNet adeptly synthesizes HDR images that reflect both bright and dark areas with improved fidelity over existing state-of-the-art methods. This synthesis makes full use of hierarchical features while employing global residual learning strategies.

Quantitative and Qualitative Evaluation

The paper reports experimental results across different datasets, highlighting that AHDRNet achieves superior quantitative and qualitative outcomes compared to existing methods. Performance is measured through various metrics including PSNR and HDR-VDP-2, with AHDRNet surpassing alternatives such as deep learning-based approaches and other algorithmic HDR methods. The competitive edge of the approach is notably underlined through its ability to handle large motion and saturation areas effectively, providing high-quality visual outputs even without pre-alignment via optical flow.

Implications and Future Directions

The successful deployment of AHDRNet emphasizes the potential of attention mechanisms in enhancing HDR imaging processes. This advancement suggests broader applications in areas requiring high-fidelity imaging under challenging conditions, such as autonomous vehicles, security surveillance, and mobile photography.

Looking towards the future, integrating more advanced attention architectures or exploring transformer models could further improve the adaptability and accuracy of HDR imaging systems. Additionally, expanding the training datasets to include diverse and complex scenes would bolster the robustness of these networks in real-world applications.

This paper delineates a significant stride in neural network-based HDR imaging, paving the way for more resilient, reliable, and high-quality imaging solutions that cater to dynamic environments plagued by saturation and motion artifacts.