“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a two-scale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer capturing small scale details. A novel guided filtering-based weighted average technique is proposed to make full use of spatial consistency for fusion of the base and detail layers. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance for fusion of multispectral, multifocus, multimodal, and multiexposure images.

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Image Fusion With Guided Filtering

A general framework for image fusion based on multi-scale transform and sparse representation

FusionGAN: A generative adversarial network for infrared and visible image fusion

Infrared and visible image fusion methods and applications: A survey

Multifocus image fusion using the nonsubsampled contourlet transform

Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review

Cross-Modality Deep Feature Learning for Brain Tumor Segmentation

While many RGB-based saliency detection algorithms have recently shown the capability of segmenting salient objects from an image, they still suffer from unsatisfactory performance when dealing with complex scenarios, insufficient illumination or occluded appearances. To overcome this problem, this article studies RGB-T saliency detection, where we take advantage of thermal modality’s robustness against illumination and occlusion. To achieve this goal, we revisit feature fusion for mining intrinsic RGB-T saliency patterns and propose a novel deep feature fusion network, which consists of the multi-scale, multi-modality, and multi-level feature fusion modules. Specifically, the multi-scale feature fusion module captures rich contexture features from each modality feature, while the multi-modality and multi-level feature fusion modules integrate complementary features from different modality features and different level of features, respectively. To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments on the RGB-T saliency detection benchmark. The experimental results demonstrate that our approach outperforms other state-of-the-art methods and the conventional feature fusion modules by a large margin.

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Revisiting Feature Fusion for RGB-T Salient Object Detection

Integration of multi-level contextual information, such as feature maps and side outputs, is crucial for Convolutional Neural Networks (CNNs)-based salient object detection. However, most existing methods either simply concatenate multi-level feature maps or calculate element-wise addition of multi-level side outputs, thus failing to take full advantages of them. In this paper, we propose a new strategy for guiding multi-level contextual information integration, where feature maps and side outputs across layers are fully engaged. Specifically, shallower-level feature maps are guided by the deeper-level side outputs to learn more accurate properties of the salient object. In turn, the deeper-level side outputs can be propagated to high-resolution versions with spatial details complemented by means of shallower-level feature maps. Moreover, a group convolution module is proposed with the aim to achieve high-discriminative feature maps, in which the backbone feature maps are divided into a number of groups and then the convolution is applied to the channels of backbone feature maps within each group. Eventually, the group convolution module is incorporated in the guidance module to further promote the guidance role. Experiments on three public benchmark datasets verify the effectiveness and superiority of the proposed method over the state-of-the-art methods.

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Qiang Zhang

Papers

Multifocus image fusion using the nonsubsampled contourlet transform

Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review

Cross-Modality Deep Feature Learning for Brain Tumor Segmentation

Revisiting Feature Fusion for RGB-T Salient Object Detection

Deep Salient Object Detection With Contextual Information Guidance