Abstract
High-dynamic-range (HDR) image has a wide range of applications, but its access is limited. Multi-exposure image fusion techniques have been widely concerned because they can obtain images similar to HDR images. In order to solve the detail loss of multi-exposure image fusion (MEF) in image reconstruction process, exposure moderate evaluation and relative brightness are used as joint weight functions. On the basis of the existing Laplacian pyramid fusion algorithm, the improved weight function can capture the more accurate image details, thereby making the fused image more detailed. In 20 sets of multi-exposure image sequences, six multi-exposure image fusion methods are compared in both subjective and objective aspects. Both qualitative and quantitative performance analysis of experimental results confirm that the proposed multi-scale decomposition image fusion method can produce high-quality HDR images.
Highlights
Due to the limited dynamic range of imaging equipment, it is impossible for existing imaging equipment to capture all the details in one scene with a single exposure
The second section describes the overall process of the fusion algorithm; The third section is a detailed explanation of the weight function; The fourth section describes the process of image Gaussian pyramid decomposition and Laplace pyramid decomposition; The fifth section is the experimental results and analysis; The sixth section is the summary of this article
GD, PMEF and the algorithm proposed in this article can maintain the uniformity of Algorithm 1 | Multi-exposure image fusion algorithm based on improved weight function
Summary
Due to the limited dynamic range of imaging equipment, it is impossible for existing imaging equipment to capture all the details in one scene with a single exposure. Multi-Exposure Image Fusion transform (SIFT) (Liu et al, 2016) to obtain both contrast and spatial consistency weights based on local gradient information. Moriyama et al (2019) proposed to use the light conversion method of preserving hue and saturation to generate a new multi exposure image for fusion, realize brightness conversion based on local color correction, and obtain the fused image by weighted average (weight is calculated by saturation). Ulucan et al (2020) proposed a new, simple and effective still image exposure fusion method This technique uses weight map extraction based on linear embedding and watershed masking. Combined with pyramid multi-scale decomposition, images with different resolutions are fused to generate the required high dynamic range image. The second section describes the overall process of the fusion algorithm; The third section is a detailed explanation of the weight function; The fourth section describes the process of image Gaussian pyramid decomposition and Laplace pyramid decomposition; The fifth section is the experimental results and analysis; The sixth section is the summary of this article
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