Infrared and visible image fusion using multiscale directional nonlocal means filter

Qin, Hanlin; Li, Jia; Zhou, Huixin; Zong, Jingguo; Zeng, Qingjie

doi:10.1364/ao.54.004299

Cited by 35 publications

(5 citation statements)

References 26 publications

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“…Cross bilateral filtering (CBF) [15], guided filtering, mean-median filtering [23], Gaussian and bilateral filtering [30], isotropic diffusion, and anisotropic diffusion [14] Choose-max [14], average gradient, local energy, and window based [34] Introduces reversal artifacts and halo effects…”

Section: Edge Preserving-based Filtering Methodsmentioning

confidence: 99%

“…For decades, numerous signal processing algorithms have been contemplated in image fusion to extract features such as multi-scale transform-based methods [10,11], sparse-representation-based [12,13], edge-preserving filtering [14,15], neural network [16], subspace-based [17] and hybrid-based methods [3]. During the last five years, deep learning (DL)-based [18] methods have received painstaking attention in image fusion due to their outstanding feature extraction from source images.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer Learning

Bhutto

Tian

et al. 2022

Remote Sensing

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Deep learning (DL) has achieved significant attention in the field of infrared (IR) and visible (VI) image fusion, and several attempts have been made to enhance the quality of the final fused image. It produces better results than conventional methods; however, the captured image cannot acquire useful information due to environments with poor lighting, fog, dense smoke, haze, and the noise generated by sensors. This paper proposes an adaptive fuzzy-based preprocessing enhancement method that automatically enhances the contrast of images with adaptive parameter calculation. The enhanced images are then decomposed into base and detail layers by anisotropic diffusion-based edge-preserving filters that remove noise while smoothing the edges. The detailed parts are fed into four convolutional layers of the VGG-19 network through transfer learning to extract features maps. These features maps are fused by multiple fusion strategies to obtain the final fused detailed layer. The base parts are fused by the PCA method to preserve the energy information. Experimental results reveal that our proposed method achieves state-of-the-art performance compared with existing fusion methods in a subjective evaluation through the visual experience of experts and statistical tests. Moreover, the objective assessment parameters are conducted by various parameters (FMI, SSIMa, API, EN, QFAB, and NFAB) which were used in the comparison method. The proposed method achieves 0.2651 to 0.3951, 0.5827 to 0.8469, 56.3710 to 71.9081, 4.0117 to 7.9907, and 0.6538 to 0.8727 gain for FMI, SSIMa, API, EN, and QFAB, respectively. At the same time, the proposed method has more noise reduction (0.3049 to 0.0021) that further justifies the efficacy of the proposed method than conventional methods.

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Section: Edge Preserving-based Filtering Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer Learning

Bhutto

Tian

et al. 2022

Remote Sensing

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“…The more widely used methods in the field of infrared and visible image fusion can be roughly classified into MST-based methods [4], sparse representation-based methods [5], spatial domain-based methods [6], and deep learning-based methods [7]. At present, the most researched and applied methods are MST-based methods, including wavelet transform [8], Laplacian pyramid transform [9], nonsubsampled shear wave transform [10], and nonsubsampled contourlet transform [11].…”

Section: Introductionmentioning

confidence: 99%

Infrared and Visible Image Fusion Based on Iterative Control of Anisotropic Diffusion and Regional Gradient Structure

Ji¹,

Zhang²,

Lin³

et al. 2022

Journal of Sensors

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To improve the fusion performance of infrared and visible images and effectively retain the edge structure information of the image, a fusion algorithm based on iterative control of anisotropic diffusion and regional gradient structure is proposed. First, the iterative control operator is introduced into the anisotropic diffusion model to effectively control the number of iterations. Then, the image is decomposed into a structure layer containing detail information and a base layer containing residual energy information. According to the characteristics of different layers, different fusion schemes are utilized. The structure layer is fused by combining the regional structure operator and the structure tensor matrix, and the base layer is fused through the Visual Saliency Map. Finally, the fusion image is obtained by reconstructing the structure layer and the energy layer. Experimental results show that the proposed algorithm can not only effectively deal with the fusion of infrared and visible images but also has high efficiency in calculation.

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“…However, if the color and brightness of an object concerned are different from the background only slightly, it can be difficult to recognize the object visually, whereas infrared radiation (IR) images captured over the object may help to provide a precise representation of the target. Hence, the fusion of visible and IR images can present more information for both human inspection and computer-based image analysis with one integrated image [3]. Indeed, image fusion has recently been utilized as effective tools in object recognition [4], remote sensing [5], target tracking [6], surveillance [7], and defense applications that require the use of multiple images of a scene.…”

Section: Introductionmentioning

confidence: 99%

Image fusion via nonlocal sparse K-SVD dictionary learning

Bai

et al. 2016

Appl. Opt.

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Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

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Infrared and visible image fusion using multiscale directional nonlocal means filter

Cited by 35 publications

References 26 publications

An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer Learning

An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer Learning

Infrared and Visible Image Fusion Based on Iterative Control of Anisotropic Diffusion and Regional Gradient Structure

Image fusion via nonlocal sparse K-SVD dictionary learning

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