Visible Algorithm Research Articles

An infrared and visible image fusion algorithm based on ResNet-152

An infrared and visible image fusion algorithm based on ResNet-152

Infrared and Visible Image Fusion Algorithm Based on Dynamic Range Compression Enhancement and NSST

Infrared and Visible Image Fusion Algorithm Based on Dynamic Range Compression Enhancement and NSST

Research and Prospect on Key Technologies of Indoor Positioning Based on Visible Light Communication

Indoor positioning has become a research hotspot because of its important application value in industrial production and daily life. Traditional wireless positioning technologies such as Wi Fi and Bluetooth are difficult to achieve high-precision indoor positioning due to electromagnetic interference and multipath effect. The modulated white LED can not only meet the needs of lighting, but also transmit the location information to achieve high-precision indoor positioning. This paper first introduces several modulation methods commonly used in visible light positioning system, compares the characteristics of different modulation methods, and proposes a modulation method suitable for visible light positioning; Then, two demodulation methods of the visible light positioning system are introduced and discussed; After that, several visible light location algorithms are introduced, and the performance of each algorithm is analyzed in detail; Finally, the problems in visible light positioning are discussed and prospected.

Research on Visible Light Indoor Localization Algorithm Based on Elman Neural Network

Research on Visible Light Indoor Localization Algorithm Based on Elman Neural Network

Unsupervised Infrared Image and Visible Image Fusion Algorithm Based on Deep Learning

Unsupervised Infrared Image and Visible Image Fusion Algorithm Based on Deep Learning

K-means+SSA-Elman network visible light indoor location awareness algorithm

K-means+SSA-Elman network visible light indoor location awareness algorithm

Multiscale Progressive Fusion of Infrared and Visible Images

Infrared and visible image fusion aims to generate more informative images of a given scene by combining multimodal images with complementary information. Although recent learning-based approaches have shown significant fusion performance, developing an effective fusion algorithm that can preserve complementary information while preventing bias toward either of the source images remains a significant challenge. In this work, we propose a multiscale progressive fusion (MPFusion) algorithm that extracts and progressively fuses multiscale features of infrared and visible images. The proposed algorithm consists of two networks, IRNet and FusionNet, which extract the intrinsic features of infrared and visible images, respectively. We transfer the multiscale information of the infrared image from IRNet to FusionNet to generate an informative fusion result. To this end, we develop the multi-dilated residual block (MDRB) and the progressive fusion block (PFB), which progressively combines the multiscale features from IRNet with those from FusionNet to fuse complementary features effectively and adaptively. Furthermore, we exploit edge-guided attention maps to preserve complementary edge information in the source images during fusion. Experimental results on several datasets demonstrate that the proposed algorithm outperforms state-of-the-art infrared and visible image fusion algorithms on both quantitative and qualitative comparisons.

Injected Infrared and Visible Image Fusion via $L_{1}$ Decomposition Model and Guided Filtering

In this paper, an infrared (IR) and visible (VIS) image fusion algorithm is designed for the injection of the IR objects into the VIS background in a perceptual manner. It consists of four parts: image decomposition, layer fusion, image reconstruction, and image refinement. An edge-preserving filter is constructed for image decomposition, in which an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> regularization term and a fractional gradient are newly introduced. The resulting filter is capable of not only preserving edges, but also attenuating the influence of the IR background. A two-layer fusion rule is adopted, which consists of a routine weighted-average fusion rule and an injected fusion rule. It ensures that the fused image is with both rich background information of the VIS image and the salient features of the IR image. After image reconstruction, the guided filter is applied again to the IR image to refine the fused image, such that the final version of the fused image is with satisfactory human visual perception under even dim lights. The effectiveness and superiority of our fusion algorithm are illustrated by the results of ablation studies and comparative experiments.

End to End Infrared and Visible Image Fusion With Texture Details and Contrast Information

Infrared and visible image fusion combine data information from different sensors to achieve a richer description of the same scene. In order to highlight the salient features of the infrared image and the visible image in the fusion image and obtain a fusion image with good performance, an end-to-end infrared and visible image fusion algorithm is proposed in this paper. The contrast attention module and visible image cascade part are introduced in the generator, so that the fusion image can focus on the detail information in the visible image and the contrast information in the infrared image. And in order to retain more structural contour information in the original image, the contour loss is added to the content loss function. In addition, the contrast and detail information in infrared and visible images are balanced by two discriminators. And a goal-guided reward function is introduced into the discriminator, which further facilitates the generator to produce effective fused images. Finally, extensive fusion experiments on public datasets verify the advantages of the proposed algorithm compared with other classical algorithms, and ablation experiments demonstrate the effectiveness of the improved part of the algorithm.

Adaptive Visible Light Location Algorithm Based on Region Division

Adaptive Visible Light Location Algorithm Based on Region Division

Electrical Fault Detection Equipment Based on Infrared Image Fusion

Maintaining the safe and stable operation of power equipment is the first task of power system operation and maintenance. The equipment in normal operation may have various faults, so it needs to be located, detected and repaired. In order to improve the on-line detection efficiency of electrical equipment, diagnose the operation state of equipment in time and locate faults, this project proposes a multi focus infrared and visible image fusion algorithm based on NSST and adaptive PCNN. Taking the power equipment as the research object, the infrared image of the power equipment is compared with the visible image, and the feature points are selected, so as to realize the image registration. After image registration, the registered visible and infrared images are fused and reconstructed, and the effective features of the two kinds of images are integrated into one image. The target image constructed by fusion not only has thermal energy information, but also has object texture, highlights the target, and can detect the fault location without power failure. It has certain practical significance for quickly removing the fault point of power equipment.

Infrared and Visible Image Fusion via Fast Approximate Bilateral Filter and Local Energy Characteristics

Image fusion is to effectively enhance the accuracy, stability, and comprehensiveness of information. Generally, infrared images lack enough background details to provide an accurate description of the target scene, while visible images are difficult to detect radiation under adverse conditions, such as low light. People hoped that the richness of image details can be improved by using effective fusion algorithms. In this paper, we propose an infrared and visible image fusion algorithm, aiming to overcome some common defects in the process of image fusion. Firstly, we use fast approximate bilateral filter to decompose the infrared image and visible image to obtain the small-scale layers, large-scale layer, and base layer. Then, the fused base layer is obtained based on local energy characteristics, which avoid information loss of traditional fusion rules. The fused small-scale layers are acquired by selecting the absolute maximum, and the fused large-scale layer is obtained by summation rule. Finally, the fused small-scale layers, large-scale layer, and base layer are merged to reconstruct the final fused image. Experimental results show that our method retains more detailed appearance information of the fused image and achieves good results in both qualitative and quantitative evaluations.

Infrared and visible image fusion through hybrid curvature filtering image decomposition

To improve the saliency of the target in the integrated image and the ability to retain the information of detail, an infrared and visible image fusion algorithm based on image decomposition of hybrid curvature filtering is proposed. Firstly, we introduce the hybrid curvature filtering to decompose the input source images into three levels, and three image layers are acquired. According to the different attributes of the image layers, this paper adopts different fusion strategies to fuse the image layers. Finally, the fusion image is obtained by way of adding the image layers. Experimental results demonstrate that compared with other algorithms, the fusion image generated from this algorithm is more conducive to human visual perception and computer analysis.

Multi-scale saliency measure and orthogonal space for visible and infrared image fusion

For infrared and visible image fusion technology, it has always been a challenge to effectively select useful information from the source image and integrate them because the imaging principle of infrared and visible images are widely different. To solve this problem, a novel infrared and visible image fusion algorithm are proposed, which includes the following contributions: (i) an infrared visual saliency extracting scheme using global measurement are presented, (ii) a visible visual saliency measure scheme by a local measurement strategy are proposed, and (iii) a fusion rule based on orthogonal space is designed to combine the extracted saliency maps. Specifically, in order to make humans pay attention to infrared targets, coarse-scale decomposition is performed. Then a global measurement strategy is utilized to get saliency maps. In addition, since visible images have rich textures, fine-scale decomposition can make the visual system pay attention to tiny details. Next, the visual saliency is measured by a local measurement strategy. Different from the general fusion rules, the orthogonal space is constructed to integrate the saliency maps, which can remove the correlation of saliency maps to avoid mutual interference. Experiments on public databases demonstrate that the fusion results of the proposed fusion algorithm are better than other comparison algorithms in qualitative and quantitative assessment.

Seam Carving based visible watermarking robust to removal attacks

Visible watermarking offers direct copyright protection to the naked eye using visible pattern superimposed in the host image. However, due to their exposure, the watermark pattern becomes an easy target to remove by malicious entities that generates copyright-free images. Until now, in the scientific literature several semi-automatic visible watermark removal attacks have been proposed, showing enough visual quality of the recovered host image. In this paper, we propose a watermark design based on modified Seam Carving technique to provide robustness against removal attacks including inpainting-based attacks, Independent Component Analysis (ICA)-based attacks, and image matting-based attacks. Robustness against the attacks is measured using common image quality assessments and a watermark visibility assessment based on the Human Visual System (HVS). Experimental results show robustness of the proposed method against above mentioned watermark removal attacks under the most severe situation, in which adversary has the logotype used as a watermark pattern because it is public in Internet. The proposed method can be applied to any visible watermarking algorithms to make them robust against illegal removal attempts, without sacrificing their original functionality, such as reversibility for authorized users.

Research on Reversible Visible Watermarking Algorithms Based on Vectorization Compression Method

Abstract In current research on reversible visible watermarking algorithm, the original visible watermark image plays an important auxiliary role, and some algorithms also entirely depend on it to restore host image without any distortion. Therefore, in order to realize semi-blind reversible visible watermarking algorithm, the conventional reversible watermarking algorithm is used to embed compressed visible watermark image data into non-visible-watermarked region of host image. However, the amount of compressed image data obtained by conventional image compression algorithm is relatively large. Therefore, a method based on vectorization compression for the visible watermark image is proposed in this paper. Firstly, it performs edge detection on visible watermark image to obtain a discrete points set $\Gamma $ of vector contour curve. Then, the discrete points in $\Gamma $ are simplified by improved Douglas–Peucker algorithm, after that it obtains compressed vector contour data of visible watermark image. In addition, a reversible visible watermarking algorithm based on convolutional relief and image alpha fusion is proposed, which realizes reversible embedding of visible watermark image and lossless restoration of host image. The experimental results show that the proposed vectorization compression method has more advantages than traditional image compression algorithms, which greatly reduces the storage space of visible watermark image with high fidelity. Additionally, the embedded watermarking image has translucent 3D relief effect, and the fusion of host image and visible watermark image becomes more natural and harmonious.

A Novel Improved Reversible Visible Image Watermarking Algorithm Based on Grad-CAM and JND

With the rapid access convenience of content brought by 5G technology, the integrity protection of content becomes more important. The reversible visible watermarking algorithm has attracted more attention due to its effective content protection. In this paper, a novel improved reversible visible image watermarking scheme based on gradient-weighted class activation mapping (Grad-CAM) and the just noticeable difference (JND) model has been presented. The proposed region of interest (ROI) selection strategy is used to locate the main protected body of images for watermark embedding. Divide the watermark and ROI into nonoverlapping blocks in the same way and then embed the classified two types of watermark blocks into corresponding ROI blocks with the JND model. The optimal bit positions for watermark embedding can be selected adaptively with JND threshold and achieve the tradeoff between the watermark visibility and watermarked image quality. For lossless image recovery and watermark extraction, the recovery information is reversibly hidden into watermarked image. In the experiments, the same process of grayscale images is used to each channel separately for color images watermarking. Besides, there are six aspects in this paper to estimate the proposed scheme; with the comparison to other reversible visible watermarking schemes, experimental results demonstrate the effectiveness of our proposed scheme.

Improving the Performance of Infrared and Visible Image Fusion Based on Latent Low-Rank Representation Nested With Rolling Guided Image Filtering

The fusion quality of infrared and visible image is very important for subsequent human understanding of image information and target processing. The fusion quality of the existing infrared and visible image fusion methods still has room for improvement in terms of image contrast, sharpness and richness of detailed information. To obtain better fusion performance, an infrared and visible image fusion algorithm based on latent low-rank representation (LatLRR) nested with rolling guided image filtering (RGIF) is proposed that is a novel solution that integrates two-level decomposition and three-layer fusion. First, infrared and visible images are decomposed using LatLRR to obtain the low-rank sublayers, saliency sublayers, and sparse noise sublayers. Then, RGIF is used to perform further multiscale decomposition of the low-rank sublayers to extract multiple detail layers, which are fused using convolutional neural network (CNN)-based fusion rules to obtain the detail-enhanced layer. Next, an algorithm based on improved visual saliency mapping with weighted guided image filtering (IVSM-GIF) is used to fuse the low-rank sublayers, and an algorithm for adaptive weighting of regional energy features based on Laplacian pyramid decomposition is used to fuse the saliency sublayers. Finally, the fused low-rank sublayer, saliency sublayer, and detail-enhanced layer are used to reconstruct the final image. The experimental results show that the proposed method outperforms other state-of-the-art fusion methods in terms of visual quality and objective evaluation, achieving the highest average values in six objective evaluation metrics.

Infrared and Visible Images Fusion Algorithm Based on NSST and IFCNN

Infrared and Visible Images Fusion Algorithm Based on NSST and IFCNN

Research on Infrared Visible Image Fusion and Target Recognition Algorithm Based on Region of Interest Mask Convolution Neural Network

Research on Infrared Visible Image Fusion and Target Recognition Algorithm Based on Region of Interest Mask Convolution Neural Network