Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review
As a result of several successful applications in computer vision and image processing,
sparse representation (SR) has attracted significant attention in multi-sensor image fusion …
sparse representation (SR) has attracted significant attention in multi-sensor image fusion …
A medical image fusion method based on convolutional neural networks
Medical image fusion technique plays an an increasingly critical role in many clinical
applications by deriving the complementary information from medical images with different …
applications by deriving the complementary information from medical images with different …
Medical image fusion via convolutional sparsity based morphological component analysis
In this letter, a sparse representation (SR) model named convolutional sparsity based
morphological component analysis (CS-MCA) is introduced for pixel-level medical image …
morphological component analysis (CS-MCA) is introduced for pixel-level medical image …
Semantic-supervised infrared and visible image fusion via a dual-discriminator generative adversarial network
Image fusion synthesizes a new image from multiple images of the same scene. The
synthesized image should be suitable for human visual perception and follow-up high-level …
synthesized image should be suitable for human visual perception and follow-up high-level …
Coconet: Coupled contrastive learning network with multi-level feature ensemble for multi-modality image fusion
Infrared and visible image fusion targets to provide an informative image by combining
complementary information from different sensors. Existing learning-based fusion …
complementary information from different sensors. Existing learning-based fusion …
A bilevel integrated model with data-driven layer ensemble for multi-modality image fusion
Image fusion plays a critical role in a variety of vision and learning applications. Current
fusion approaches are designed to characterize source images, focusing on a certain type of …
fusion approaches are designed to characterize source images, focusing on a certain type of …
Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning
Medical image fusion is important in image-guided medical diagnostics, treatment, and other
computer vision tasks. However, most current approaches assume that the source images …
computer vision tasks. However, most current approaches assume that the source images …
DRPL: Deep regression pair learning for multi-focus image fusion
In this paper, a novel deep network is proposed for multi-focus image fusion, named Deep
Regression Pair Learning (DRPL). In contrast to existing deep fusion methods which divide …
Regression Pair Learning (DRPL). In contrast to existing deep fusion methods which divide …
Multi-modal medical image fusion by Laplacian pyramid and adaptive sparse representation
Z Wang, Z Cui, Y Zhu - Computers in Biology and Medicine, 2020 - Elsevier
Multi-modal medical image fusion refers to the fusion of two or more medical images
obtained by different imaging methods into one image. Multi-modal medical images contain …
obtained by different imaging methods into one image. Multi-modal medical images contain …
Joint image fusion and denoising via three-layer decomposition and sparse representation
X Li, F Zhou, H Tan - Knowledge-Based Systems, 2021 - Elsevier
Image fusion has been received much attentions in recent years. However, solving both
noise-free image fusion and noise-perturbed image fusion problems remains a big …
noise-free image fusion and noise-perturbed image fusion problems remains a big …