Abstract
This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) images. Recent non-flow-based methods suffer from ghosting artifacts in the presence of large object motion. Flow-based methods face the same issue since their optical flow algorithms yield huge alignment errors. To eliminate ghosting artifacts, we propose a simple yet effective alignment network for solving the misalignment. The proposed pyramid inter-attention module (PIAM) performs alignment of LDR features by leveraging inter-attention maps. Additionally, to boost the representation of aligned features in the merging process, we propose a dual excitation block (DEB) that recalibrates each feature both spatially and channel-wise. Exhaustive experimental results demonstrate the effectiveness of the proposed PIAM and DEB, achieving state-of-the-art performance in terms of producing ghost-free HDR images.
Highlights
Humans can see in a wide range of lighting conditions because the human eye adjusts constantly to a broad range of natural luminance values in the environment
We propose a novel end-to-end flow-based HDR method, including pyramid inter-attention module (PIAM) and dual excitation block (DEB) for the alignment and merging processes, respectively
The main contributions of this paper can be summarized as follows: We propose a novel convolutional neural networks (CNNs)-based framework for ghost-free HDR imaging by leveraging pyramid inter-attention module (PIAM) which effectively aligns LDR images
Summary
Humans can see in a wide range of lighting conditions because the human eye adjusts constantly to a broad range of natural luminance values in the environment. Standard digital cameras typically fail to capture images with sufficient dynamic range because of the limited ranges of sensors. To alleviate this issue, high-dynamic-range (HDR) imaging has been developed to improve the range of color and contrast in captured images [1]. Given a series of low-dynamic-range (LDR) images captured at different exposures, an HDR image is produced by merging these LDR images
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