A novel super-resolution image fusion algorithm based on improved PCNN and wavelet transform

Liu, Na; Gao, Kun; Song, Yajun; Ni, Guoqiang

doi:10.1117/12.833925

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Liu et al [31] employ PCNN to fuse the LF coefficients while the structural similarity operator is used to fuse the HF coefficients in wavelet domain. Alternately, Wu et al [32] first take wavelet coefficient as the linking strength to construct adaptive PCNN, and then employ it to fuse all coefficients.…”

Section: Traditional Wavelet Transformmentioning

confidence: 99%

Review of Image Fusion Based on Pulse-Coupled Neural Network

Wang

Zhu

et al. 2015

Arch Computat Methods Eng

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Recently, many researchers have paid their more attention to image fusion technique based on pulse coupled neural network (PCNN). In order to make the researchers to rapidly understand the research development of image fusion based on PCNN, it is systematically reviewed in the paper. On the basis of statistical analysis on published papers, firstly, PCNN and some modified models are introduced. Then we review the PCNN's applications in the field of image fusion. Subsequently, some existing problems are summarized, while we give some suggestions for the future research.

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Section: Traditional Wavelet Transformmentioning

confidence: 99%

Review of Image Fusion Based on Pulse-Coupled Neural Network

Wang

Zhu

et al. 2015

Arch Computat Methods Eng

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“…Kumar et al [147] developed a supervised CNN to learn to merge the data from PET-CT images of lung cancer. CNN has also been applied to fuse medical images MRI/CT, MRI/SPECT, multiparametric MR images [148] and PET/MRI [149]. CNN can also be combined with a wavelet transform for the fusion of CT and MR images [150].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

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“…For instance, the Canny operator can extract edge contours from the high-frequency components obtained upon wavelet decomposition, prioritizing edge information during fusion and recombination [12]. The wavelet transform can be blended with weighted averaging to process wavelet coefficients [8,13,14]. Imagery can be divided into high-frequency and low-frequency components for processing using pyramid methods and wavelet transform, respectively [15].…”

Section: Introductionmentioning

confidence: 99%

A Curvelet-Transform-Based Image Fusion Method Incorporating Side-Scan Sonar Image Features

Zhao

Jin

Bian

et al. 2023

JMSE

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Current methods of fusing side-scan sonar images fail to tackle the issues of shadow removal, preservation of information from adjacent strip images, and maintenance of image clarity and contrast. To address these deficiencies, a novel curvelet-transform-based approach that integrates the complementary attribute of details from side-scan sonar strip images is proposed. By capitalizing on the multiple scales and orientations of the curvelet transform and its intricate hierarchical nature, myriad fusion rules were applied at the corresponding frequency levels, enabling a more-tailored image fusion technique for side-scan sonar imagery. The experimental results validated the effectiveness of this method in preserving valuable information from side-scan sonar images, reducing the presence of shadows and ensuring both clarity and contrast in the fused images. By meeting the aforementioned challenges encountered in existing methodologies, this approach demonstrated great practical significance.

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A novel super-resolution image fusion algorithm based on improved PCNN and wavelet transform

Cited by 3 publications

References 6 publications

Review of Image Fusion Based on Pulse-Coupled Neural Network

Review of Image Fusion Based on Pulse-Coupled Neural Network

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

A Curvelet-Transform-Based Image Fusion Method Incorporating Side-Scan Sonar Image Features

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