Ballardgodfrey4522

Image-to-image translation is to transfer images from a source domain to a target domain. Conditional Generative Adversarial Networks (GANs) have enabled a variety of applications. Initial GANs typically conclude one single generator for generating a target image. Recently, using multiple generators has shown promising results in various tasks. However, generators in these works are typically of homogeneous architectures. In this paper, we argue that heterogeneous generators are complementary to each other and will benefit the generation of images. By heterogeneous, we mean that generators are of different architectures, focus on diverse positions, and perform over multiple scales. To this end, we build two generators by using a deep U-Net and a shallow residual network, respectively. The former concludes a series of down-sampling and up-sampling layers, which typically have large perception field and great spatial locality. In contrast, the residual network has small perceptual fields and works well in characterizing details, especially textures and local patterns. Afterwards, we use a gated fusion network to combine these two generators for producing a final output. Tofacitinib The gated fusion unit automatically induces heterogeneous generators to focus on different positions and complement each other. Finally, we propose a novel approach to integrate multi-level and multi-scale features in the discriminator. This multi-layer integration discriminator encourages generators to produce realistic details from coarse to fine scales. We quantitatively and qualitatively evaluate our model on various benchmark datasets. Experimental results demonstrate that our method significantly improves the quality of transferred images, across a variety of image-to-image translation tasks. We have made our code and results publicly available http//aiart.live/chan/.Radial distortion has widely existed in the images captured by popular wide-angle cameras and fisheye cameras. Despite the long history of distortion rectification, accurately estimating the distortion parameters from a single distorted image is still challenging. The main reason is that these parameters are implicit to image features, influencing the networks to learn the distortion information fully. In this work, we propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency. Our key insight is that distortion rectification can be cast as a problem of learning an ordinal distortion from a single distorted image. To solve this problem, we design a local-global associated estimation network that learns the ordinal distortion to approximate the realistic distortion distribution. In contrast to the implicit distortion parameters, the proposed ordinal distortion has a more explicit relationship with image features, and significantly boosts the distortion perception of neural networks. Considering the redundancy of distortion information, our approach only uses a patch of the distorted image for the ordinal distortion estimation, showing promising applications in efficient distortion rectification. In the distortion rectification field, we are the first to unify the heterogeneous distortion parameters into a learning-friendly intermediate representation through ordinal distortion, bridging the gap between image feature and distortion rectification. The experimental results demonstrate that our approach outperforms the state-of-the-art methods by a significant margin, with approximately 23% improvement on the quantitative evaluation while displaying the best performance on visual appearance.We propose objective, image-based techniques for quantitative evaluation of facial skin gloss that is consistent with human judgments. We use polarization photography to obtain separate images of surface and subsurface reflections, and rely on psychophysical studies to uncover and separate the influence of the two components on skin gloss perception. We capture images of facial skin at two levels, macro-scale (whole face) and meso-scale (skin patch), before and after cleansing. To generate a broad range of skin appearances for each subject, we apply photometric image transformations to the surface and subsurface reflection images. We then use linear regression to link statistics of the surface and subsurface reflections to the perceived gloss obtained in our empirical studies. The focus of this paper is on within-subject gloss perception, that is, on visual differences among images of the same subject. Our analysis shows that the contrast of the surface reflection has a strong positive influence on skin gloss perception, while the darkness of the subsurface reflection (skin tone) has a weaker positive effect on perceived gloss. We show that a regression model based on the concatenation of statistics from the two reflection images can successfully predict relative gloss differences.Current RGB-D salient object detection (SOD) methods utilize the depth stream as complementary information to the RGB stream. However, the depth maps are usually of low-quality in existing RGB-D SOD datasets. Most RGB-D SOD networks trained with these datasets would produce error-prone results. In this paper, we propose a novel Complementary Depth Network (CDNet) to well exploit saliency-informative depth features for RGB-D SOD. To alleviate the influence of low-quality depth maps to RGB-D SOD, we propose to select saliency-informative depth maps as the training targets and leverage RGB features to estimate meaningful depth maps. Besides, to learn robust depth features for accurate prediction, we propose a new dynamic scheme to fuse the depth features extracted from the original and estimated depth maps with adaptive weights. What's more, we design a two-stage cross-modal feature fusion scheme to well integrate the depth features with the RGB ones, further improving the performance of our CDNet on RGB-D SOD. Experiments on seven benchmark datasets demonstrate that our CDNet outperforms state-of-the-art RGB-D SOD methods. The code is publicly available at https//github.com/blanclist/CDNet.