다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Recent advances in image fusion technology in agriculture

Joint sparse model with coupled dictionary for medical image fusion

Multi-focus image fusion aims at combining multiple partially focused images of the same scenario into an all-focused image, and one of the most effective methods for image fusion is sparse representation. Traditional sparse representation based fusion method uses all of the image patches for dictionary learning, which brings unvalued information, resulting in artifacts and extra calculating time. To remove unvalued information and build a compact dictionary, in this sparse representation based fusion approach, a novel dictionary constructing method based on joint patch grouping and informative sampling is proposed. Nonlocal similarity is introduced into joint patch grouping, and each source image is not considered independently. Patches of all source images with similar structures are flagged as a group, and only one class of informative image patch is selected in dictionary learning for simplifying the calculation. The orthogonal matching pursuit (OMP) algorithm is performed to obtain sparse coefficients, and max-L1 fusion role is adopted to reconstruct fused images. The experimental results show the superiority of the proposed approach. 

A novel sparse representation based fusion approach for multi-focus images

Image fusion in neuro diagnosis is intimidating due to its complexity. The heterogeneous natures of the original brain images make intermodal transmission difficult during fusion. Medical image fusion using complementary modalities results in loss of vital salient information. Poor fusion, colour deficiencies result due to similar processing for both the modalities. A dual technique is proposed using discrete fractional wavelet transform (FRWT) and non-subsampled directional filter banks for better extraction of salient image elements for improved diagnosis. The sparsity character of the coefficients FRWT is controlled by optimising the parity operator using Grey Wolf optimisation algorithm. Four sets of neurological multimodal magnetic resonance imaging and single photon emission computed tomography (CT) brain images are used from benchmark database for validation. The objective evaluation has been conducted using five metrics. The main values obtained from objective metrics based on the proposed technique are 6.3213 for Shannon entropy, mutual information is computed to be 2.7582, fusion factor is 1.9095, standard deviation is 0.1310, and edge strength is 0.76122 indicating improved diagnostic information and superior image quality. Subjective evaluation by a medico validates the findings with finer visual output and enhanced contrast in comparison with recent and state-of-the-art methods.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2019.0948

Medical fusion framework using discrete fractional wavelets and non-subsampled directional filter banks

On the use of joint sparse representation for image fusion quality evaluation and analysis

A feature-level full-reference image denoising quality assessment method based on joint sparse representation

Image fusion method based on deep learning has achieved great success. In order to obtain better performance, researchers keep deepening the network structure, but it also brings greater computational burden. Compared with the traditional convolutional structure, Dense Convolutional Network (DenseNet) achieves a good balance between performance and computational complexity. Therefore, in this paper, we present a novel medical image fusion method based on DenseNet, which achieves feature reuse by connecting features over channels, and enables the algorithm to achieve better performance than traditional networks with fewer parameters and calculation costs. The experimental results show that, on the premise that the convolution structure layers of the proposed method is far less than that of the state-of-the-art deep learning method, the fused image details are still well preserved, and the objective assessment indices are better than that of the traditional transformation domain methods and close to the state-of-the-art deep learning method.

Medical Image Fusion Based a Densely Connected Convolutional Networks

In this paper, we reported a novel Multi-Focus Image Fusion (MFIF) method based on regularization optimization and sparse representation model, which takes full advantages of the imaging characteristics of multi-focus images. First, a multi-source regularization optimization model was proposed to divide source images into a common background layer and respective detail layers jointly. This makes the focused details scattered in different sources are extracted in concert. Then the resulting detail layers are fused in sparse representation domain. To be more task-specific for MFIF, an atom focus degree measurement based fusion rule was proposed to highlight the focused atoms. Finally, the fusion result is reconstructed by combining the fused detail layer and the common background layer. Thorough experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms some state-of-the-art fusion methods.

Yanxiang Hu

Papers

On the use of joint sparse representation for image fusion quality evaluation and analysis

A feature-level full-reference image denoising quality assessment method based on joint sparse representation

Medical Image Fusion Based a Densely Connected Convolutional Networks

A novel multi-focus image fusion method based on joint regularization optimization layering and sparse representation

A new multi-focus image fusion method based on multi-classification focus learning and multi-scale decomposition