Pixel-level image fusion

Infrared and visible image fusion methods and applications: A survey

Multi-focus image fusion with a deep convolutional neural network

As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion over the last few years with a number of effective algorithms being proposed. However, due to the patch-based manner applied in sparse coding, most existing SR-based fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to misregistration, while these two issues are of great concern in image fusion. In this letter, we introduce a recently emerged signal decomposition model known as convolutional sparse representation (CSR) into image fusion to address this problem, which is motivated by the observation that the CSR model can effectively overcome the above two drawbacks. We propose a CSR-based image fusion framework, in which each source image is decomposed into a base layer and a detail layer, for multifocus image fusion and multimodal image fusion. Experimental results demonstrate that the proposed fusion methods clearly outperform the SR-based methods in terms of both objective assessment and visual quality.

Image Fusion With Convolutional Sparse Representation

IFCNN: A general image fusion framework based on convolutional neural network

Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure

Quadtree-based multi-focus image fusion using a weighted focus-measure

Infrared and visual image fusion through infrared feature extraction and visual information preservation

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical method widely used in image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM method based on the spatial information is proposed for IR ship target segmentation. The improvements include two parts: 1) adding the nonlocal spatial information based on the ship target and 2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov random field. In addition, the results of ${K}$ -means are used to initialize the improved FCM method. Experimental results show that the improved method is effective and performs better than the existing methods, including the existing FCM methods, for segmentation of the IR ship images.

Yu Zhang

Papers

IFCNN: A general image fusion framework based on convolutional neural network

Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure

Quadtree-based multi-focus image fusion using a weighted focus-measure

Infrared and visual image fusion through infrared feature extraction and visual information preservation

Infrared Ship Target Segmentation Based on Spatial Information Improved FCM