Optimization of Decomposition Techniques for Hybrid Wavelet based Image Fusion Algorithm using NSCT and DTCWT

Kiran, S.

doi:10.1109/icaiss55157.2022.10010828

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Figure 3 The approximate components and detail features of the effective information are incorporated in the low-pass and bandpass subbands following NSCT's decomposition of the source image. [10] Reasonably designed fusion rules are applicable to low-pass and band-pass sub-bands respectively, and the fused image obtained by NSCT inverse transformation will contain complementary information of the two images, which facilitates recognition and further processing.…”

Section: Nsct Transformationmentioning

confidence: 99%

Photoelectric pod image fusion algorithm based on LatLRR and NSCT

Zhang,

Wang

2023

Third International Conference on Optics and Communication Technology (ICOCT 2023)

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The airborne Photoelectric pod has an infrared image and a visible camera and has been widely used in a variety of fields. To more effectively fuse the infrared and viewable pictures gathered by Photoelectric pod, a latent low-rank representation (LatLRR) and NSCT-based image fusion technique is provided. The visible image is preprocessed using the bicubic interpolation method, and its resolution is unified with that of the infrared image. The initial image is decomposed employing a potential low-rank representation to yield low-rank and sparse components, and the low-rank elements are subsequently further deconstructed into low-frequency and high-frequency coefficients through the NSCT. The low-frequency coefficients are fused using adaptive weighted fusion rules, while the high frequency coefficients are fused using average gradient fusion rules. To enhance the performance of the fused mage, the information is fused using the average gradient of the image and the fused low-rank component is generated by means of the NSCT inverse transformation. The experimental results demonstrate that the algorithm suggested in this study can successfully fuse the infrared and visible images acquired by the Photoelectric pod, that the resulting image has better contrast and overall information, as well as that both subjective and objective evaluation is superior to that of other commonly employed algorithms.

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Section: Nsct Transformationmentioning

confidence: 99%

Photoelectric pod image fusion algorithm based on LatLRR and NSCT

Zhang,

Wang

2023

Third International Conference on Optics and Communication Technology (ICOCT 2023)

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“…3D target reconstruction technologies in scenes is hot off the press the field of computer vision all through the way [1] . The imaging course of traditional cameras is the operation of mapping three-dimensional points in space onto a twodimensional surface, but this operation causes the loss of depth information, which brings challenges to the acquisition of three-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Improved algorithm of TOF image fusion based on DTCWT

Yan,

Zhang,

Wang

et al. 2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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With the continuous development of TOF imaging technology, distance imaging technology has gradually been widely used as a part of three-dimensional spatial vision in many fields such as autonomous driving, mechanical obstacle avoidance, and virtual reality. However, due to the limited performance of current distance imaging sensors, the quality of depth images obtained from distance imaging is generally low, making it difficult to apply to computer vision tasks with high accuracy requirements. For the current problems of low accuracy, fewer boundary data, and poor dimensional resolution of TOF image, we introduce a TOF-CMOS image fusion approach found on DTCWT (Dual-Tree Complex Wavelet Transform). By utilizing the similarity between standard CMOS images and TOF images of the same scene in the edge area, the detailed information of the TOF images is enriched and their accuracy is improved. First, we applied the dual-tree complex wavelet transform applied to the source images. Second, we adopt a bilateral filter for the lowfrequency components of the TOF images to increase the accuracy of the images. Meanwhile, we extract the edge data in the high-frequency components of the CMOS images through the soft threshold method. Then, we blend the highfrequency components of the TOF images with the extracted high-frequency components of the CMOS images. In this paper, we innovatively propose fusion rules connecting the regional variance and the pixel maximum: we use the above two fusion methods respectively targeted according to the difference of data involved in the highest as well as the second-highest levels of high-frequency components. Finally, the images are reassembled by using the inverse dual-tree complex wavelet transform. We evaluate the merge results using evaluation indicators like average gradient, peak signalto-noise ratio, and so on. The laboratory results indicate that the method adequately enhances the visual effect of the TOF image and enriches the detailed information of the TOF image.

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