Search

Rain removal from an image is a challenging problem since no motion information can be obtained from successive images. In this work, an input image is first decomposed into low-frequency part and high-frequency part by using guided image filter. So that the rain streaks would be in the high-frequency part with non-rain textures, and then the high-frequency part is decomposed into a “rain component” and a “non-rain component” by performing dictionary learning and sparse coding. To separate rain streaks from high-frequency part, a hybrid feature set is exploited which includes histogram of gradient (HoG) and difference of depth (DoD). With the hybrid feature set applied, most rain streaks can be removed; meanwhile, non-rain components can be enhanced. Compared with the state-of-the-art method [12], our proposed approach shows that not only the rain components can be removed more effectively, but also the visual quality of restored images can be improved. [ABSTRACT FROM PUBLISHER]

Given the increasing number of mobile platforms, a key technical challenge is how to provide an optimal photo browsing experience given the limited screen size available on mobile devices. This paper proposes a novel technique for intelligent mobile image categorization on mobile platform to reduce computation complexity based on cloud computing. In this technique, captured images are analyzed to detect visual salient area, which is then classified in real-time using sparse representation. Mathematically, the derived algorithm regards the salient regions as the dictionary in sparse representation, and selects the salient regions that minimize the residual output error iteratively, thus the resulting regions have a direct correspondence to the performance requirements of the given problem. Experimental results obtained using extensive datasets captured under uncontrolled conditions show the proposed system effectively manages mobile images using sparse representation on cloud computing. [ABSTRACT FROM PUBLISHER]

Given the increasing number of mobile platforms, a key technical challenge is how to provide efficient image processing application on resource-limited mobile devices. This paper proposes a novel technique for mobile image abstraction on mobile platform. In order to reduce the computation complexity, the image abstraction process is conducted on a cloud computing system. In this technique, captured images are analyzed to detect visual salient area, which is then provided for adaptive detail preservation of abstraction. To further reduce the image size for efficient transmission between mobile device and cloud computing system while maintaining the visual quality of abstracted image, the derived algorithm regards the salient regions as the dictionary in sparse representation, and selects the salient regions that minimize the residual output error iteratively, thus the resulting regions have a direct correspondence to the performance requirements of the given problem. Experimental results obtained using extensive datasets captured under uncontrolled conditions show the efficacy of the proposed system for conducting image abstraction on mobile platform using sparse representation. [ABSTRACT FROM PUBLISHER]