Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Liu, Shuaiqi; Ma, Jian; Yin, Lu; Li, Hailiang; Cong, Shuang; Ma, Xiaole; Hu, Shaohai

doi:10.1109/access.2020.2993404

Cited by 18 publications

(14 citation statements)

References 44 publications

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“…In this experiment, we use the following nine metrics to quantitatively evaluate the performance of the image fusion algorithms: (1) Normalized mutual information (NMI), which can effectively improve the stability of the MI (Liu et al, 2020 ). (2) Nonlinear correlation information entropy (NCIE), which is a metric used to evaluate the quality of the fusion image (Su et al, 2022 ).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Multi-focus image fusion dataset and algorithm test in real environment

et al. 2022

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Section: Experimental Results and Analysismentioning

confidence: 99%

Multi-focus image fusion dataset and algorithm test in real environment

et al. 2022

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“…Finally, the fusion image was obtained by weighting the source image according to the decision graph. Experimental results show that the algorithm can preserve image details well and maintain spatial consistency [98].…”

Section: Combination Of Different Transformsmentioning

confidence: 96%

Multi-Sensor Image Fusion: A Survey of the State of the Art

Li¹,

Yu²,

Zhang³

et al. 2021

JCC

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Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.

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“…Image colors are restored by controlling saturation, and image contrast between different channels is also improved. Image fusion is an important method used in image defogging, which can effectively improve the image contrast, detail information and so on (Jin et al, 2020 ; Liu et al, 2020 ). In the same scene, since the imaging equipment cannot focus different depth objects at the same time, so multi-focus image fusion technology is used to extract different focus areas from multiple images to synthesize a clear image (Jin et al, 2018b ; Liu et al, 2019b ).…”

Section: Related Workmentioning

confidence: 99%

Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

Liu

Luo

et al. 2021

Front. Neurorobot.

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Bad weather conditions (such as fog, haze) seriously affect the visual quality of images. According to the scene depth information, physical model-based methods are used to improve image visibility for further image restoration. However, the unstable acquisition of the scene depth information seriously affects the defogging performance of physical model-based methods. Additionally, most of image enhancement-based methods focus on the global adjustment of image contrast and saturation, and lack the local details for image restoration. So, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion. First, a single foggy image is processed by gamma correction to obtain a set of underexposed images. Then the saturation of the obtained underexposed and original images is enhanced. Next, each image in the multi-exposure image set (including the set of underexposed images and the original image) is decomposed into the base and detail layers by a guided filter. The base layers are first decomposed into image patches, and then the fusion weight maps of the image patches are constructed. For detail layers, the exposure features are first extracted from the luminance components of images, and then the extracted exposure features are evaluated by constructing gaussian functions. Finally, both base and detail layers are combined to obtain the defogged image. The proposed method is compared with the state-of-the-art methods. The comparative experimental results confirm the effectiveness of the proposed method and its superiority over the state-of-the-art methods.

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Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Cited by 18 publications

References 44 publications

Multi-focus image fusion dataset and algorithm test in real environment

Multi-focus image fusion dataset and algorithm test in real environment

Multi-Sensor Image Fusion: A Survey of the State of the Art

Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

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