Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

For the low computational efficiency, the existence of false targets, blurred targets, and halo occluded targets of existing image fusion models, a novel fusion method of visible and infrared images using GE-WA model and VGG-19 network is proposed. First, Laplacian is used to decompose the visible and infrared images into basic images and detail content. Next, a Gaussian estimation function is constructed, and a basic fusion scheme using the GE-WA model is designed to obtain a basic fusion image that eliminates halo of visible image. Then, the pre-trained VGG-19 network and the multi-layer fusion strategy are used to extract the fusion of different depth features of the visible and infrared images, and also obtain the fused detail content with different depth features. Finally, the fusion image is reconstructed by the basic image and detail content after fusion. The experiments show that the comprehensive evaluation FQ of the proposed method is better than other comparison methods, and has better performance in the aspects of image fusion speed, halo elimination of visible image, and image fusion quality, which is more suitable for visible and infrared image fusion in complex environments.

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“… where is the index value of structural similarity. is the operator of structural similarity 43 . Deviation Index (DI).…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Fusion of visible and infrared images using GE-WA model and VGG-19 network

Fan

Zhong-chao

2023

Sci Rep

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“…The deep reinforcement learning algorithm constrains the learning behavior of the policy network by taking the objective function of the value network in the Actor Critical (AC) algorithm as the penalty item of the policy gradient algorithm to improve the stability and performance of the algorithm. This algorithm effectively limits the error accumulation caused by sub optimal strategy execution when the value network pair estimation is highly inaccurate, and improves the VR scene detail enhancement effect [20][21][22].…”

Section: Details Enhancement Methods Based On Deep Reinforcement Lear...mentioning

confidence: 99%

VR Scene Detail Enhancement Method Based on Depth Reinforcement Learning Algorithm

Feng,

Tong,

Zhu

et al. 2024

Int J Comput Intell Syst

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In virtual reality, due to factors such as light sources and surface materials of objects, the details of the scene exhibit extremely complex changes, making it difficult to capture environmental modeling relationships and reducing the quality of scene details. Therefore, a VR scene detail enhancement method based on deep reinforcement learning algorithm is proposed. Using the Total Variation (TV) denoising algorithm to decompose the VR scene, the VR scene is divided into two parts: noisy and non-noisy, to complete the noise removal. Based on the denoised VR scene, a Hessian matrix is constructed to extract VR scene features using the SURF algorithm. Using deep reinforcement learning algorithms to train and process the extracted VR visual features, introducing meta-learning within the gradient descent method, updating the parameters of the deep reinforcement learning Expose framework, and accelerating the training speed of the deep reinforcement learning Expose framework. By designing L1 loss, structural similarity loss, content perception loss, and sharpness loss functions, the enhancement effect of VR visual details can be improved. The experimental results show that the proposed method enhances the gray-scale values and distribution of VR scene detail images to be higher and more uniform. When the magnification is different, the studied method can effectively enhance the signal-to-noise ratio of VR scenes. Therefore, it indicates that the VR scene detail enhancement effect of the method proposed in this article is good.

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“…However, these mathematically predefined methods are not scalable for diverse illumination environments. Deep-learningbased LLIE methods, which have been shown to outperform traditional methods [53,54], have been increasingly adopted to visual SLAM [55][56][57]. Therefore, in this paper, modern deep-learning-based LLIE methods are experimentally investigated to enhance the visual SLAM-based construction mapping approach, concerning the illumination-variant conditions at planetary worksites.…”

Section: Low-light Image Enhancement Methodsmentioning

confidence: 99%

Robotic Mapping Approach under Illumination-Variant Environments at Planetary Construction Sites

et al. 2022

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In planetary construction, the semiautonomous teleoperation of robots is expected to perform complex tasks for site preparation and infrastructure emplacement. A highly detailed 3D map is essential for construction planning and management. However, the planetary surface imposes mapping restrictions due to rugged and homogeneous terrains. Additionally, changes in illumination conditions cause the mapping result (or 3D point-cloud map) to have inconsistent color properties that hamper the understanding of the topographic properties of a worksite. Therefore, this paper proposes a robotic construction mapping approach robust to illumination-variant environments. The proposed approach leverages a deep learning-based low-light image enhancement (LLIE) method to improve the mapping capabilities of the visual simultaneous localization and mapping (SLAM)-based robotic mapping method. In the experiment, the robotic mapping system in the emulated planetary worksite collected terrain images during the daytime from noon to late afternoon. Two sets of point-cloud maps, which were created from original and enhanced terrain images, were examined for comparison purposes. The experiment results showed that the LLIE method in the robotic mapping method significantly enhanced the brightness, preserving the inherent colors of the original terrain images. The visibility and the overall accuracy of the point-cloud map were consequently increased.

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Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform

Cited by 6 publications

References 24 publications

Fusion of visible and infrared images using GE-WA model and VGG-19 network

Fusion of visible and infrared images using GE-WA model and VGG-19 network

VR Scene Detail Enhancement Method Based on Depth Reinforcement Learning Algorithm

Robotic Mapping Approach under Illumination-Variant Environments at Planetary Construction Sites

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