DT-CWT: Feature level image fusion based on dual-tree complex wavelet transform

Malik, S. Saleem; Kumar, Sanjeev; Maruthi, G. B.

doi:10.1109/icices.2014.7033982

Cited by 5 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is the lowest-level fusion operation, targeting each pixel, which results in considerably complex computation. Featurelevel fusion involves feature integration for a certain area of an image object or a certain feature such as color or edge [40]; the favorable factors of each feature are integrated for the classifier to use. Therefore, feature-level fusion can highlight certain types of targets in a targeted manner, which is beneficial for highlighting image details.…”

Section: Dff Modulementioning

confidence: 99%

DMFFNet: Dual-Mode Multi-Scale Feature Fusion-Based Pedestrian Detection Method

Rui

Ouyang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Most contemporary pedestrian detection algorithms are based on visible light image detection. However, in environments with dim light, small targets, and easily occluded and cluttered backgrounds, single-mode visible light images relying on color, texture, and other features cannot adequately represent the feature information of targets; as a result, a large number of targets are lost and the algorithm performance is not good. To address this problem, we propose a dual-modal multi-scale feature fusion network (DMFFNet). First, we use the MobileNet v3 backbone network to extract the features of dual-modal images as input for the multi-scale fusion attention (MFA) module, combining the idea of multi-scale feature fusion and attention mechanism. Second, we deeply fuse the multi-scale features output by the MFA with the double deep feature fusion (DDFF) module to enhance the semantic and geometric information of the target. Finally, we optimize the loss function to reflect the distance between the predicted box and the real box more realistically as well as to enhance the ability of the network toward predicting difficult samples. We performed multi-directional evaluations on the KAIST dual-light pedestrian dataset and the visible-thermal infrared pedestrian dataset (VTI) in our laboratory through comparative and ablation experiments. The overall MR -2 on the KAIST duallight pedestrian dataset is 9.26%, and the MR -2 in dim light, partial occlusion, and severe occlusion are 5.17%, 23.35%, and 47.31%, respectively. The overall MR -2 on the VIT dual-light pedestrian dataset is 9.26%, and the MR -2 in dim light, partial occlusion, and severe occlusion are 5.17%, 23.35%, and 47.31%, respectively. The results show that the algorithm performs well on pedestrian detection, especially in dim light and when the target was occluded.

show abstract

Section: Dff Modulementioning

confidence: 99%

DMFFNet: Dual-Mode Multi-Scale Feature Fusion-Based Pedestrian Detection Method

Rui

Ouyang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The goal of image fusing is to merge important details (features) from several images into one containing important information. The existing image fusion approaches are divided into three types: pixel-level fusion [1][2][3], feature-level fusion [4][5][6], and decision-level fusion [7]. Merging images at the pixel level merges pixels in a linear or non-linear manner.…”

Section: Related Workmentioning

confidence: 99%

Pre-processing and data fusion of a complex image obtained by a pair of sensors with a free arrangement

Semenishchev

Voronin

Zelensky

et al. 2023

Optoelectronic Imaging and Multimedia Technology IX

View full text Add to dashboard Cite

The article an approach to improve the accuracy of restoring the boundaries of objects obtained to create 3D structures by The paper proposes a data processing algorithm that allows performing primary processing operations in order to identify the main parameters and fusion them into a single image. To form a complex image, it is possible to first enter the parameters selected by the operator, which corrects the mixing ratio. As a noise reduction algorithm for different ranges, the multi-criteria filtering method is used, which is based on minimizing the sum of the squared deviations of the input signal and the generated estimate, as well as the sum of the squared differences of the obtained estimates. Using the adjustment factor allows you to set the degree of influence of the criterion on the resulting processing. Using this method also allows you to detect the boundaries of objects. The search for the border is based on the analysis of frequency components and the search for sharp changes in color gradation. The possibility of applying this approach for various types of data is shown on the example of processing parallel streams. For the primary construction of areas of significance, an algorithm for changing the range of clusters and an object complexity analyzer are used. The analyzer is built on the basis of calculating the weighted value of the number of color gradient transitions per unit area. To visually improve the quality of the data, a color space conversion algorithm based on alpha mixed is used. As test data used to evaluate the effectiveness, pairs of test images are used, obtained by sensors fixed at resolution of 1024x768 (8 bit, color image) and far-IR spectrum 320x240 (8 bit, color image). Images of simple shapes are used as analyzed objects.

show abstract

Infrared and Visible Image Fusion Based on Morphological Image Enhancement of Dual-Tree Complex Wavelet

Liu

Zhang

2019

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

View full text Add to dashboard Cite

DT-CWT: Feature level image fusion based on dual-tree complex wavelet transform

Cited by 5 publications

References 5 publications

DMFFNet: Dual-Mode Multi-Scale Feature Fusion-Based Pedestrian Detection Method

DMFFNet: Dual-Mode Multi-Scale Feature Fusion-Based Pedestrian Detection Method

Pre-processing and data fusion of a complex image obtained by a pair of sensors with a free arrangement

Infrared and Visible Image Fusion Based on Morphological Image Enhancement of Dual-Tree Complex Wavelet

Contact Info

Product

Resources

About