Multi-focus image fusion based on block matching in 3D transform domain

Yang, Dongsheng; Hu, Shaohai; Liu, Shuaiqi; Ma, Xiaole; Sun, Yuchao

doi:10.21629/jsee.2018.02.21

Cited by 21 publications

(3 citation statements)

References 16 publications

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“…Additionally, we do not provide any in-depth discussion of the influence of different fusion rules on the coefficients. According to the correlation among the high-frequency coefficients in the same scale but different directions, Equation (1) is used to select coefficients with relatively large values [32]. Wfalse^k,l(i,j)={Ak,l(i,j)|true∑l=1LAk(i,j)|>|true∑l=1LBk(i,j)|Bk,l(i,j)otherwise…”

Section: Methodsmentioning

confidence: 99%

Denoising Method for Passive Photon Counting Images Based on Block-Matching 3D Filter and Non-Subsampled Contourlet Transform

Wang

Yin

Gao

et al. 2019

Sensors

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Multi-pixel photon counting detectors can produce images in low-light environments based on passive photon counting technology. However, the resulting images suffer from problems such as low contrast, low brightness, and some unknown noise distribution. To achieve a better visual effect, this paper describes a denoising and enhancement method based on a block-matching 3D filter and a non-subsampled contourlet transform (NSCT). First, the NSCT was applied to the original image and histogram-equalized image to obtain the sub-band low- and high-frequency coefficients. Regional energy and scale correlation rules were used to determine the respective coefficients. Adaptive single-scale retinex enhancement was applied to the low-frequency components to improve the image quality. The high-frequency sub-bands whose line features were best preserved were selected and processed using a symbol function and the Bayes-shrink threshold. After applying the inverse transform, the fused photon counting image was subjected to an improved block-matching 3D filter, significantly reducing the operation time. The final result from the proposed method was superior to those of comparative methods in terms of several objective evaluation indices and exhibited good visual effects and details from the objective impression.

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Section: Methodsmentioning

confidence: 99%

Denoising Method for Passive Photon Counting Images Based on Block-Matching 3D Filter and Non-Subsampled Contourlet Transform

Wang

Yin

Gao

et al. 2019

Sensors

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“…These methods can be categorized into two types: conventional and deep learning-based methods. Conventional medical image fusion techniques can be divided into two subtypes: spatial domain and frequency domain methods [ 11 ]. The spatial domain fusion technique manipulates image pixels directly with simple rules (such as maximum), but is less effective.…”

Section: Related Workmentioning

confidence: 99%

BPDGAN: A GAN-Based Unsupervised Back Project Dense Network for Multi-Modal Medical Image Fusion

Liu

Yang

2022

Entropy

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Single-modality medical images often cannot contain sufficient valid information to meet the information requirements of clinical diagnosis. The diagnostic efficiency is always limited by observing multiple images at the same time. Image fusion is a technique that combines functional modalities such as positron emission computed tomography (PET) and single-photon emission computed tomography (SPECT) with anatomical modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) to supplement the complementary information. Meanwhile, fusing two anatomical images (like CT-MRI) is often required to replace single MRI, and the fused images can improve the efficiency and accuracy of clinical diagnosis. To this end, in order to achieve high-quality, high-resolution and rich-detail fusion without artificial prior, an unsupervised deep learning image fusion framework is proposed in this paper. It is named the back project dense generative adversarial network (BPDGAN) framework. In particular, we construct a novel network based on the back project dense block (BPDB) and convolutional block attention module (CBAM). The BPDB can effectively mitigate the impact of black backgrounds on image content. Conversely, the CBAM improves the performance of BPDGAN on the texture and edge information. To conclude, qualitative and quantitative experiments are tested to demonstrate the superiority of BPDGAN. In terms of quantitative metrics, BPDGAN outperforms the state-of-the-art comparisons by approximately 19.58%, 14.84%, 10.40% and 86.78% on AG, EI, Qabf and Qcv metrics, respectively.

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“…The methods based on the transform domain are generally implemented by three steps: image multi-scale decomposition, fusion of the coefficients generated by the transformation, and multi-scale reconstruction based on the fused coefficients. Among the representative algorithms is image fusion based on Laplace pyramid transform [7], discrete wavelet transform [8], curvelet transform [9], contourlet transform [10], and shearlet transform [11] and so on. Spatial domain-based methods include image fusion based on: max-min filtering [12], image block matching [13], guided filter [14], dense-scale feature-invariant methods [15] and multi-focus image fusion algorithms based on boundary discovery [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Liu

Yin

et al. 2020

IEEE Access

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Multi-focus image fusion is an image processing that generates an integrated image by merging multiple images from different focus area in the same scene. For most fusion methods, the detection of the focus area is a critical step. In this paper, we propose a multi-focus image fusion algorithm based on a dual convolutional neural network (DualCNN), in which the focus area is detected from super-resolved images. Firstly, the source image is input into a DualCNN to restore the details and structure from its superresolved image, as well as to improve the contrast of the source image. Secondly, the bilateral filter is used to reduce noise on the fused image, and the guided filter is used to detect the focus area of the image and refine the decision map. Finally, the fused image is obtained by weighting the source image according to the decision map. Experimental results show that our algorithm can well retain image details and maintain spatial consistency. Compared with existing methods in multiple groups of experiments, our algorithm can achieve better visual perception according to subjective evaluation and objective indexes. INDEX TERMS Multi-focus image fusion, super-resolution, dual convolutional neural network, focus area detection.

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Multi-focus image fusion based on block matching in 3D transform domain

Cited by 21 publications

References 16 publications

Denoising Method for Passive Photon Counting Images Based on Block-Matching 3D Filter and Non-Subsampled Contourlet Transform

Denoising Method for Passive Photon Counting Images Based on Block-Matching 3D Filter and Non-Subsampled Contourlet Transform

BPDGAN: A GAN-Based Unsupervised Back Project Dense Network for Multi-Modal Medical Image Fusion

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

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