Image fusion using dual tree discrete wavelet transform and weights optimization

Aghamaleki, Javad Abbasi; Ghorbani, Alireza

doi:10.1007/s00371-021-02396-9

Cited by 21 publications

(4 citation statements)

References 45 publications

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“…The WT provides more detailed information about the time and location of the frequencies and includes the segmentation technique at this point, where the Fourier transform is inadequate. Thus, the WT facilitates signal analysis in both time and frequency domains (Aghamaleki and Ghorbani, 2021). The WT can also be used to reduce the size of an image.…”

Section: Wavelet Transformmentioning

confidence: 99%

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Akdoğan,

Özer,

Oğuz

2024

RIA

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Purpose Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV). Design/methodology/approach Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV. Findings In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%. Originality/value An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

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Section: Wavelet Transformmentioning

confidence: 99%

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Akdoğan,

Özer,

Oğuz

2024

RIA

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“…Among them, the multi-scale transform-based fusion framework is mainly a method to decompose the source image into different scales of information and analyze and fuse them at these scales. Commonly used multi-scale transform methods include wavelet transforms [ 6 , 7 , 8 ] (e.g., discrete wavelets), pyramid transforms [ 9 , 10 , 11 , 12 ] (e.g., gaussian pyramid or laplace pyramid), and methods based on multi-scale geometric analysis [ 13 , 14 , 15 ] (e.g., contour transform or curve transform). These transform methods are able to capture both detailed and global information in an image and have different applicability to different types of images.…”

Section: Related Workmentioning

confidence: 99%

“…There are more current infrared and visible image fusion methods, but they are mainly categorized into two groups: traditional methods and deep learning (DL)-based methods. Traditional fusion methods are usually based on fusion in the spatial and transform domains [ 5 ], and the image fusion frameworks used mainly include multi-scale transform (MST)-based fusion frameworks [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ], sparse representation (SR)-based fusion frameworks [ 16 , 17 , 18 ], subspace-based fusion frameworks [ 19 , 20 , 21 ], saliency-based fusion frameworks [ 22 ], and hybrid fusion frameworks [ 23 , 24 , 25 ]. And according to the adopted network architecture, the DL-based image fusion methods can be mainly categorized into three groups, which are autoencoders (AE)-based image fusion frameworks [ 26 , 27 , 28 , 29 , 30 ], convolutional neural network (CNN)-based image fusion frameworks [ 31 , 32 , 33 , 34 , 35 , 36 , 37 ] and generative adversarial network-(GAN) based image fusion frameworks [ 38 , 39 , 40 , 41 , 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

Semantic-Aware Fusion Network Based on Super-Resolution

Xu,

Zou

2024

Sensors

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The aim of infrared and visible image fusion is to generate a fused image that not only contains salient targets and rich texture details, but also facilitates high-level vision tasks. However, due to the hardware limitations of digital cameras and other devices, there are more low-resolution images in the existing datasets, and low-resolution images are often accompanied by the problem of losing details and structural information. At the same time, existing fusion algorithms focus too much on the visual quality of the fused images, while ignoring the requirements of high-level vision tasks. To address the above challenges, in this paper, we skillfully unite the super-resolution network, fusion network and segmentation network, and propose a super-resolution-based semantic-aware fusion network. First, we design a super-resolution network based on a multi-branch hybrid attention module (MHAM), which aims to enhance the quality and details of the source image, enabling the fusion network to integrate the features of the source image more accurately. Then, a comprehensive information extraction module (STDC) is designed in the fusion network to enhance the network’s ability to extract finer-grained complementary information from the source image. Finally, the fusion network and segmentation network are jointly trained to utilize semantic loss to guide the semantic information back to the fusion network, which effectively improves the performance of the fused images on high-level vision tasks. Extensive experiments show that our method is more effective than other state-of-the-art image fusion methods. In particular, our fused images not only have excellent visual perception effects, but also help to improve the performance of high-level vision tasks.

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“…Stationary Wavelet Transform (SWT) solves the problem of shift-invariance, thus contributing to preserving more detailed information in the decomposition coefficients [39]. A dual-tree complex Wavelet Transform shows improved performance in computational efficiency, near shift-invariance, and directional selectivity due to a separable filter bank [40]. Lifting Wavelet Transform has the advantages of adaptive design, irregular sampling, and integral transform over DWT [41].…”

Section: Related Workmentioning

confidence: 99%

Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN

Ben-Shoushan

Brook

2023

Remote Sensing

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Smart vehicles with embedded Autonomous Vehicle (AV) technologies are currently equipped with different types of mounted sensors, aiming to ensure safe movement for both passengers and other road users. The sensors’ ability to capture and gather data to be synchronically interpreted by neural networks for a clear understanding of the surroundings is influenced by lighting conditions, such as natural lighting levels, artificial lighting effects, time of day, and various weather conditions, such as rain, fog, haze, and extreme temperatures. Such changing environmental conditions are also known as complex environments. In addition, the appearance of other road users is varied and relative to the vehicle’s perspective; thus, the identification of features in a complex background is still a challenge. This paper presents a pre-processing method using multi-sensorial RGB and thermal camera data. The aim is to handle issues arising from the combined inputs of multiple sensors, such as data registration and value unification. Foreground refinement, followed by a novel statistical anomaly-based feature extraction prior to image fusion, is presented. The results met the AV challenges in CNN’s classification. The reduction of the collected data and its variation level was achieved. The unified physical value contributed to the robustness of input data, providing a better perception of the surroundings under varied environmental conditions in mixed datasets for day and night images. The method presented uses fused images, robustly enriched with texture and feature depth and reduced dependency on lighting or environmental conditions, as an input for a CNN. The CNN was capable of extracting and classifying dynamic objects as vehicles and pedestrians from the complex background in both daylight and nightlight images.

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Image fusion using dual tree discrete wavelet transform and weights optimization

Cited by 21 publications

References 45 publications

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Semantic-Aware Fusion Network Based on Super-Resolution

Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN

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