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Images contain many levels of important structures and edges. Compared to masses of research to make filters edge preserving, finding scale-aware local operations was seldom addressed in a practical way, al-beit similarly vital in image processing and computer vision. We propose a new framework to filter images with the complete control of detail smoothing(More)
We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major contribution is to propose a valve linkage function (VLF)(More)
Color, infrared, and flash images captured in different fields can be employed to effectively eliminate noise and other visual artifacts. We propose a two-image restoration framework considering input images in different fields, for example, one noisy color image and one dark-flashed near infrared image. The major issue in such a framework is to handle(More)
Images now come in different forms – color, near-infrared, depth, etc. – due to the development of special and powerful cameras in computer vision and computational photography. Their cross-modal correspondence establishment is however left behind. We address this challenging dense matching problem considering structure variation possibly existing in these(More)
Previous joint/guided filters directly transfer the structural information in the reference image to the target one. In this paper, we first analyze its major drawback -- that is, there may be completely different edges in the two images. Simply passing all patterns to the target could introduce significant errors. To address this issue, we propose the(More)
Color, infrared and flash images captured in different fields can be employed to effectively eliminate noise and other visual artifacts. We propose a two-image restoration framework considering input images from different fields, for example, one noisy color image and one dark-flashed near-infrared image. The major issue in such a framework is to handle all(More)
Current color-to-gray methods compute the grayscale results by preserving the discriminability among individual pixels. However, human perception tends to firstly group the perceptually similar elements while looking at an image, according to the Gestalt principles. In this paper, we propose a novel two-scale approach for converting color images to(More)
(a) Input Image (b) Segmentation (c) Stylization (d) Depth-of-field (e) Cartoon Figure 1: Our highly accurate automatic portrait segmentation method allows many portrait processing tools to be fully automatic. (a) is the input image and (b) is our automatic segmentation result. (c-e) show different automatic image stylization applications based on the(More)