Visible and NIR image fusion using weight-map-guided Laplacian–Gaussian pyramid for improving scene visibility

Vanmali, Ashish V.; Gadre, Vikram M.

doi:10.1007/s12046-017-0673-1

Cited by 85 publications

(45 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The final fused images are obtained by inverse transforms. There are several representative transforms for infrared and visible image fusion: pyramid transforms [28][29][30], wavelet transforms [31][32][33], contourlet transforms, and non-subsampled contourlet transform [12,34,35]. SR [36] is a novel image representation theory that simulates the sparse coding mechanism of human vision systems [37].…”

Section: Conventional Fusion Methodsmentioning

confidence: 99%

Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Infrared and visible image fusion can obtain combined images with salient hidden objectives and abundant visible details simultaneously. In this paper, we propose a novel method for infrared and visible image fusion with a deep learning framework based on a generative adversarial network (GAN) and a residual network (ResNet). The fusion is accomplished with an adversarial game and directed by the unique loss functions. The generator with residual blocks and skip connections can extract deep features of source image pairs and generate an elementary fused image with infrared thermal radiation information and visible texture information, and more details in visible images are added to the final images through the discriminator. It is unnecessary to design the activity level measurements and fusion rules manually, which are now implemented automatically. Also, there are no complicated multi-scale transforms in this method, so the computational cost and complexity can be reduced. Experiment results demonstrate that the proposed method eventually gets desirable images, achieving better performance in objective assessment and visual quality compared with nine representative infrared and visible image fusion methods.

show abstract

Section: Conventional Fusion Methodsmentioning

confidence: 99%

Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…First, are static image fusion algorithms, developed over the last 30 years, where fusion is performed frame by frame to form the fused video sequence. The most popular and widely used algorithms are the Laplacian pyramid fusion [25,27] and Wavelet transform [46,47]. Further to these classic algorithms, new multi-scale techniques have more recently been proposed based on the static fusion using Curvelets [50], Ridgelets [51], Contourlets [14], Shearlet [3] as well as the Dual tree complex wavelet transform (DTCWT) [48].…”

Section: Video Fusionmentioning

confidence: 99%

“…Fusing multi-resolution pyramids rather than complete image signals, provides greater flexibility when choosing relevant information for fused image, allowing the selection of spatially overlapping features from different inputs, if they occupy different scale ranges. The most common multi-resolution techniques are the Laplacian pyramid (LAP) [25,27], ROLP or Contrast pyramid [26,45], Discrete wavelet transform (DWT) [46][47][48], Shift invariant discrete wavelet (SIDWT) [21], bilateral filter [11], guided filter [12,13], Shearlet Transform [3], Nonsubsampled contourlet transform [14] etc.…”

Section: Introductionmentioning

confidence: 99%

Rolling 3D Laplacian Pyramid Video Fusion

Pavlović

Petrović

2019

Electronics

View full text Add to dashboard Cite

In this paper, we present a novel algorithm for video fusion of multi-sensor sequences applicable to real-time night vision systems. We employ the Laplacian pyramid fusion of a block of successive frames to add temporal robustness to the fused result. For the fusion rule, we first group high and low frequency levels of the decomposed frames in the block from both input sensor sequences. Then, we define local space-time energy measure to guide the selection based fusion process in a manner that achieves spatio-temporal stability. We demonstrate our approach on several well-known multi-sensor video fusion examples with varying contents and target appearance and show its advantage over conventional video fusion approaches. Computational complexity of the proposed methods is kept low by the use of simple linear filtering that can be easily parallelised for implementation on general-purpose graphics processing units (GPUs).

show abstract

“…Capturing infrared images is also advantageous for obtaining details and edge information that are not generally acquired in the VIS spectrum. Many types of algorithms for fusing VIS and NIR images have been developed [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Visible and Near-Infrared Image Synthesis Using PCA Fusion of Multiscale Layers

2020

View full text Add to dashboard Cite

This study proposes a method of blending visible and near-infrared (NIR) images to enhance their edge details and local contrast based on the Laplacian pyramid and principal component analysis (PCA). In the proposed method, both the Laplacian pyramid and PCA are implemented to generate a radiance map. Using the PCA algorithm, the soft-mixing method and the mask-skipping filter were applied when the images were fused. The color compensation method uses the ratio between the radiance map fused by the Laplacian pyramid and the PCA algorithm and the luminance channel of the visible image to preserve the chrominance of the visible image. The results show that the proposed method improves edge details and local contrast effectively.

show abstract

Visible and NIR image fusion using weight-map-guided Laplacian–Gaussian pyramid for improving scene visibility

Cited by 85 publications

References 30 publications

Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network

Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network

Rolling 3D Laplacian Pyramid Video Fusion

Visible and Near-Infrared Image Synthesis Using PCA Fusion of Multiscale Layers

Contact Info

Product

Resources

About