Weighted Sparse Representation and Gradient Domain Guided Filter Pyramid Image Fusion Based on Low-Light-Level Dual-Channel Camera

Guo, Chen; Li, Li; Jin, Weiqi; Qiu, Su; Guo, Hui

doi:10.1109/jphot.2019.2935134

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…To identify the absolute‐maxima value of an image

f ((), x, y)

on a closed region R, the critical points are assigned inside the image

italicΔf ((), x, y)

as zero. Then, maximize the

f ((), x, y)

on the boundary region of R. Comparison of points from the input image points, the image point having highest value is estimated as an absolute‐maxima value 58

italicΔf ((), x, y) = \max_{0 \leq x \leq n} f ((), x, y)

…”

Section: Proposed Framework For Underwater Image Restorationmentioning

confidence: 99%

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

Sivamani

Nedumaran

2021

Color Research & Application

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Underwater image processing is a vast area of research to identify blurred objects in the ocean or sea environments. The light absorbed and scattered by the underwater medium create artifacts in the acquired images due to low‐light effects, poor visibility, darkness, illumination variation, and color contrast degradation. In this research work, a novel optical image preprocessing framework was proposed to restore the color contrast of images obtained from various water‐colored environments. Domain transform‐based multiscale image decomposition method was used to extract the base, residual, and detail layers. Then, the smoothed image was reconstructed from the base layer by applying the adaptive iterative backward‐projection algorithm. To obtain dehazed images, a multiscale fusion‐based algorithm was developed, which fused the detail layers and reconstructed the images. The outcome of the proposed framework revealed that the contrast degradation and low‐light‐related artifacts present in the acquired images from various water‐colored environments were found to be considerably reduced. The experimental results of the proposed method exhibit scalable improvements over the other methods in terms of visual perception and estimated performance metrics (underwater image quality measure = 6.1186; peak signal‐to‐noise ratio = 26.68; feature points matching = 96 points; semantic image segmentation accuracy = 0.8465 and processing time of 0.11 seconds on a single image). The novelty of this method relies on the fusion of well‐established techniques, which outperformed the advanced techniques like deep learning method in terms of simplicity, speed, performance, datasets, image resolution, and applications.

show abstract

“…To identify the absolute‐maxima value of an image

f ((), x, y)

on a closed region R, the critical points are assigned inside the image

italicΔf ((), x, y)

as zero. Then, maximize the

f ((), x, y)

on the boundary region of R. Comparison of points from the input image points, the image point having highest value is estimated as an absolute‐maxima value 58

italicΔf ((), x, y) = \max_{0 \leq x \leq n} f ((), x, y)

…”

Section: Proposed Framework For Underwater Image Restorationmentioning

confidence: 99%

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

Sivamani

Nedumaran

2021

Color Research & Application

View full text Add to dashboard Cite

show abstract

“…The purpose of image decomposition is to extract image contours and detailed layers. CNN is used to classify and fuse the detailed layer, while the weighted sparse representation (wSR) [21] method proposed by our previous work is used to fuse the contour layer. The advantage of decomposition is to reduce the difficulty of CNN classification, reduce the network complexity, and improve the accuracy of CNN classification.…”

Section: Decomposition Convolution Neural Network Fusion Algoritmentioning

confidence: 99%

High-Dynamic Range, Night Vision, Image-Fusion Algorithm Based on a Decomposition Convolution Neural Network

Guo

Jin

et al. 2019

IEEE Access

Self Cite

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The dynamic range of night vision scenes is typically very large. Owing to the limited dynamic range of the traditional low-light-level imaging technology, the captured images are always partially overexposed or underexposed. Multi-exposure fusion is the most effective method for overcoming the dynamic range limitations of sensors. Recently, deep learning has achieved tremendous progress in many fields. However, only a few breakthroughs have been reported on high-dynamic image fusion with the deep learning method. Additionally, many problems have been reported in conjunctions with commonly used deep-learning methods. In this study, a high-dynamic image fusion algorithm is proposed based on the decomposition convolution neural network and weighted sparse representation. Based on image decomposition, the problem of the acquisition in training samples in network training can be solved. Therefore, the classification accuracy of the network can be improved. Additionally, the decomposition structure reduces the workload of each layer and improves the efficiency and quality of the image fusion outcome. INDEX TERMS Convolution neural network, image fusion, high-dynamic range, low-light-level.

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“…In particular, NSST has attracted more attention because of its superior computational efficiency to NSCT. Compared with pyramidbased methods, such as Gaussian pyramid decomposition, Laplacian pyramid decomposition [9], and gradient pyramid transformation [10], the method based on NSST can be decomposed from multiple directions, thus obtaining more image details. Compared with wavelet methods, such as discrete wave and dual tree complex wavelet, the method based on NSST can represent the curve and edge details of image well.…”

Section: Introductionmentioning

confidence: 99%

Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain

Guo

Xie

et al. 2022

Complex Intell. Syst.

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Multimodal medical image is an effective method to solve a series of clinical problems, such as clinical diagnosis and postoperative treatment. In this study, a medical image fusion method based on convolutional sparse representation (CSR) and mutual information correlation is proposed. In this method, the source image is decomposed into one high-frequency and one low-frequency sub-band by non-subsampled shearlet transform. For the high-frequency sub-band, CSR is used for high-frequency coefficient fusion. For the low-frequency sub-band, different fusion strategies are used for different regions by mutual information correlation analysis. Analysis of two kinds of medical image fusion problems, namely, CT–MRI and MRI–SPECT, reveals that the performance of this method is robust in terms of five common objective metrics. Compared with the other six advanced medical image fusion methods, the experimental results show that the proposed method achieves better results in subjective vision and objective evaluation metrics.

show abstract

Weighted Sparse Representation and Gradient Domain Guided Filter Pyramid Image Fusion Based on Low-Light-Level Dual-Channel Camera

Cited by 12 publications

References 29 publications

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

High-Dynamic Range, Night Vision, Image-Fusion Algorithm Based on a Decomposition Convolution Neural Network

Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain

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