Abstract In image fusion approaches, feature extraction and processing are key tasks, and the fusion performance is directly affected by the different features and processing methods undertaken. However, most of deep learning-based methods use deep features directly without them. This leads to the fusion performance degradation in some cases. To solve these drawbacks, in our paper, a deep features and zero-phase component analysis (ZCA) based novel fusion framework is proposed. Firstly, the residual network (ResNet) is used to extract deep features from source images. Then ZCA and l 1 -norm are utilized to normalize the deep features and obtain initial weight maps. The final weight maps are obtained by employing a soft-max operation in association with the initial weight maps. Finally, the fused image is reconstructed using a weighted-averaging strategy. Compared with the existing fusion methods, experimental results demonstrate that the proposed framework achieves better performance in both objective assessment and visual quality. The code of our fusion algorithm is available at https: github.com hli1221 imagefusion_resnet50 .
@cite_27: Universal style transfer aims to transfer arbitrary visual styles to content images. Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality. In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles. The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network. The whitening and coloring transforms reflect direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer. We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods. We also analyze our method by visualizing the whitened features and synthesizing textures by simple feature coloring.
@cite_27 proposed a universal style transfer algorithm using ZCA operation to transfer the style of artistic image into content image. The encoder network is used to obtain the style features( @math ) and content features( @math ). Then authors use ZCA operation to project @math and @math into the same space. The final transferred features will be obtained by a coloring transform method which is a reverse operation to the ZCA operation. Finally, the styled image is obtained by transferred features and a decoder network.