Journal ArticleDOI
Multi-focus image fusion with deep residual learning and focus property detection
Reads0
Chats0
TLDR
In this paper , a residual architecture that includes a multi-scale feature extraction module and a dual-attention module is designed as the basic unit of a deep convolutional network, which is firstly used to obtain an initial fused image from the source images.About:
This article is published in Information Fusion.The article was published on 2022-06-01. It has received 8 citations till now. The article focuses on the topics: Computer science & Focus (optics).read more
Citations
More filters
Journal ArticleDOI
ZMFF: Zero-shot multi-focus image fusion
TL;DR: ZMFF as discussed by the authors is the first unsupervised and untrained deep model for multi-focus image fusion, which consists of a deep image prior network to model the deep prior of the fused image and a deep mask prior network for each source image.
Journal ArticleDOI
Gradient-based multi-focus image fusion using foreground and background pattern recognition with weighted anisotropic diffusion filter
G. Tirumala Vasu,P. Palanisamy +1 more
Journal ArticleDOI
Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain
TL;DR: In this paper , a novel multi-focus image fusion method via local energy and sparse representation in the shearlet domain is proposed, where the source images are decomposed into low and high-frequency sub-bands according to the Shearlet transform.
Journal ArticleDOI
Multi-Focus Image Fusion for Full-Field Optical Angiography
TL;DR: Wang et al. as mentioned in this paper proposed an FFOA image fusion method based on the nonsubsampled contourlet transform and contrast spatial frequency, which significantly expands the range of focus of optical angiography and can be effectively extended to public multi-focused datasets.
Journal ArticleDOI
Yes, DLGM! A novel hierarchical model for hazard classification
TL;DR: The experimental results prove that DLGM has promising aptitudes for hazard classification and that FSGM(1, 1) and HFFNN are effective and the research can contribute added value and support to the daily practice in industrial safety.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Posted Content
Squeeze-and-Excitation Networks
TL;DR: Squeeze-and-excitation (SE) as mentioned in this paper adaptively recalibrates channel-wise feature responses by explicitly modeling interdependencies between channels, which can be stacked together to form SENet architectures.
Journal ArticleDOI
Loss Functions for Image Restoration With Neural Networks
TL;DR: It is shown that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged, and a novel, differentiable error function is proposed.
Journal ArticleDOI
Image Fusion With Guided Filtering
Shutao Li,Xudong Kang,Jianwen Hu +2 more
TL;DR: Experimental results demonstrate that the proposed method can obtain state-of-the-art performance for fusion of multispectral, multifocus, multimodal, and multiexposure images.