Removing rain from a single image via Convolutional Neural Network

受恶劣天气的影响，室外视觉系统所获得的图像会劣化。雨是常见的恶劣天气之一，目前国内外关于去雨的问题已有一些解决的方案，但大多关注于视频去雨。由于该类方法是以丰富的时空相关信息为前提，因而并不适用于单幅图像去雨。近年来，单幅图像去雨的研究逐渐受到重视，然而现有方法需要在去雨效果和图像清晰度之间折中且计算效率低下，难以满足实际应用需求。 为此，本文针对单幅图像去雨，基于变分法和卷积神经网络提出三种新的单幅图像去雨算法，主要研究内容及成果如下： 1.提出基于梯度正则化的单幅图像去雨算法。首先设计一个引导平滑滤波器实现初步去雨。该滤波器在保证输出图像与输入图像一致性的前提下，引入梯度正则项，使其根...Affected by the bad weather, the images obtained by outdoor visual systems always degrade. Rain is one of the common bad weather. At home and abroad, there are some solutions about removal of rain, but most of it aims to videos. It can’t apply to single image since no temporal information can be obtained. Recently, the study of rain removal from a single image gradually receive more attention. Ho...学位：工学硕士院系专业：信息科学与技术学院_信号与信息处理学号：2332013115323

Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks

Bilateral filters have wide spread use due to their edge-preserving properties. The common use case is to manually choose a parametric filter type, usually a Gaussian filter. In this paper, we will generalize the parametrization and in particular derive a gradient descent algorithm so the filter parameters can be learned from data. This derivation allows to learn high dimensional linear filters that operate in sparsely populated feature spaces. We build on the permutohedral lattice construction for efficient filtering. The ability to learn more general forms of high-dimensional filters can be used in several diverse applications. First, we demonstrate the use in applications where single filter applications are desired for runtime reasons. Further, we show how this algorithm can be used to learn the pairwise potentials in densely connected conditional random fields and apply these to different image segmentation tasks. Finally, we introduce layers of bilateral filters in CNNs and propose bilateral neural networks for the use of high-dimensional sparse data. This view provides new ways to encode model structure into network architectures. A diverse set of experiments empirically validates the usage of general forms of filters

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed