A Novel Patch-Based Multi-Exposure Image Fusion Using Super-Pixel Segmentation

Wang, Shupeng; Zhao, Yao

doi:10.1109/access.2020.2975896

Cited by 20 publications

(25 citation statements)

References 35 publications

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“…The detailed information of IR and VI images is extracted to determine the performance of the fusion algorithm. The source image and fused images by all algorithms are shown in Figures 6 (a (2) The second set of source images is "Bristol Queen's road" with a resolution of 280×360. The person sheltered by trees in the IR image is clearly visible, but the details of the surrounding environment are blurred.…”

Section: ) Experimental Results and Discussionmentioning

confidence: 99%

“…Image fusion is a technology that performs registration on images obtained by different sensors on the same target; then, it utilizes certain algorithms to remove redundant information and integrate complementary information to generate more suitable fusion images for human visual perception [1]. Recently, image fusion technology obtains a boost and plays a key role in image segmentation and computer vision [2]- [4]. With the development of image fusion technology and reduction of hardware costs, higher reliability and comprehensiveness are expected for image fusion.…”

Section: Introductionmentioning

confidence: 99%

“…+ are the prediction coefficients and update filter coefficients of LSWT, as defined in equations (1)-(2).…”

mentioning

confidence: 99%

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An Infrared and Visible Image Fusion Algorithm Based on LSWT-NSST

Jiang

2020

IEEE Access

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Regarding the problems of image distortion, edge blurring, Gibbs phenomena in the traditional wavelet transform algorithm and the loss of subtle features in the Non-Subsampled Shearlet Transform (NSST), and considering the physical characteristics of infrared and visible images, an infrared and visible image fusion algorithm based on the Lifting Stationary Wavelet Transform (LSWT) and Non-Subsampled Shearlet Transform is proposed in this paper. First, since LSWT can quickly calculate and has all advantages of traditional WT, it is utilized to decompose infrared and visible images to obtain lowfrequency coefficients and multi-scale and multi-directional high-frequency coefficients, respectively. Second, NSST multi-scale decomposition is used to extract the target features and detailed features of the image from the high and low-frequency sub-bands to obtain new high and low-frequency sub-bands. Third, according to the physical characteristics that low and high-frequency coefficients represent, different fusion rules are designed. Discrete Cosine Transform (DCT) and Local Spatial Frequency (LSF) are introduced in the low-frequency sub-band, and LSF adaptive weighted fusion rules are used in the DCT domain. The fusion strategy improves the regional contrast in the high-frequency sub-band with the spectral characteristics of human vision. Finally, the Inverse Lifting Stationary Wavelet Transform (ILSWT) is used to reconstruct the fusion coefficients to obtain the final fused images. To verify the advantages of the proposed algorithm in this paper, the classic and advanced 9 IR and VI fusion algorithms are selected for subjective and objective comparison. In the objective evaluation, a comprehensive ranking index is designed based on 9 classical indicators. Simulation experiments with 10 IR and VI fusion algorithms prove that the proposed algorithm has better performance and flexibility. The results show that the proposed algorithm in this paper fuses the images with clear edges, prominent targets, and good visual perception, and it outperforms state-of-the-art image fusion algorithms.

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Section: ) Experimental Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Infrared and Visible Image Fusion Algorithm Based on LSWT-NSST

Jiang

2020

IEEE Access

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“…This function can retain the details in bright and dark regions and improve the fusion quality while maintaining a high computational speed. Wang [34] proposed an adaptive image patch segmentation method that used superpixel segmentation to divide the input images into non-overlapping patches composed of pixels with similar visual properties. Compared with the existing MEF methods that used fixed-size image patches, it avoided the patch effect and preserved the color properties of the source images.…”

Section: Patch-based Methodsmentioning

confidence: 99%

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

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Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image processing and computer vision, which can integrate images with multiple exposure levels into a full exposure image of high quality. It is an economical and effective way to improve the dynamic range of the imaging system and has broad application prospects. In recent years, with the further development of image representation theories such as multi-scale analysis and deep learning, significant progress has been achieved in this field. This paper comprehensively investigates the current research status of MEF methods. The relevant theories and key technologies for constructing MEF models are analyzed and categorized. The representative MEF methods in each category are introduced and summarized. Then, based on the multi-exposure image sequences in static and dynamic scenes, we present a comparative study for 18 representative MEF approaches using nine commonly used objective fusion metrics. Finally, the key issues of current MEF research are discussed, and a development trend for future research is put forward.

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“…However, if the block size is not sufficiently small, this technique may produce artifacts on the scene boundaries. S. Wang [27] presented a novel multi-exposure image fusion method which works on patch segmentation. In this method they have used super-pixel segmentation then it is decomposed into three components and fused independently that is based on the human vision.…”

Section: Related Workmentioning

confidence: 99%

A Novel De-Ghosting Image Fusion Technique for Multi-Exposure, Multi-Focus Images Using Guided Image Filtering

et al. 2020

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In this paper, a novel de-ghosting image fusion technique is presented, which enhances the quality of low dynamic range images using multi-level exposures taken from the ordinary camera and also removes the ghosting artifact. In the proposed algorithm, first, the source images, taken under different exposure settings, are decomposed into base and detail layers using two-scale decomposition. The base and detail layers contain small and large-scale variation details of the source images, respectively. The Laplacian-of-Gaussian filter is applied to the source images to get the edge information. Afterward, the saliency map of the edges is computed. To remove the ghosting artifacts, a weight matrix is calculated by applying the median filter on the histogram equalized source images. The weight matrix is combined with the saliency map to generate more accurate weights. The separate weights for the base and detail layers are calculated using guided image filters. Finally, the base and detail layers' weights are fused with the source images to generate a vivid and enhanced image without any artifacts. The proposed technique is evaluated both qualitatively and quantitatively. The comparison of our technique in terms of Yang's Metric (Q Y ), Quality Mutual Information (Q M I ), Gradient-based Fusion Metric (Q G ) and Chen Blum's Metric (Q CB ) with other state-of-the-art techniques proves that the proposed technique outperforms existing techniques.

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A Novel Patch-Based Multi-Exposure Image Fusion Using Super-Pixel Segmentation

Cited by 20 publications

References 35 publications

An Infrared and Visible Image Fusion Algorithm Based on LSWT-NSST

An Infrared and Visible Image Fusion Algorithm Based on LSWT-NSST

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

A Novel De-Ghosting Image Fusion Technique for Multi-Exposure, Multi-Focus Images Using Guided Image Filtering

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