The video sequences degraded by fog suffer from poor visibility. In this paper, we present a Contrast Limited Adaptive Histogram Equalization(CLAHE)-based method to remove fog. CLAHE establishes a maximum value to clip the histogram and redistributes the clipped pixels equally to each gray level. It can limit the noise while enhancing the contrast. First, the background image is extracted from the video sequence. And then the moving pixels are estimated and bounded into foreground images. Second, the foreground and background images are defogged respectively by CLAHE. Third, the foreground and background images are fused into the new frames. Finally, the defogged video sequence is obtained. We experiment with a video sequence degraded by fog to evaluate the effectiveness of our method. And the histogram statistics are applied in comparison with traditional method. The results show that our method is more effective than traditional method. In addition, our method can fill the requirement of real-time.

Fog Removal from Video Sequences Using Contrast Limited Adaptive Histogram Equalization

A particle swarm optimization based simultaneous learning framework for clustering and classification

Single image dehazing using a new color channel

We present a new approach to remove haze from surveillance video sequences. This approach extracts the background image through the frame differential method, uses the dark channel prior to estimate the atmospheric light, and then calculates a universal transmission map on the intensity component of the background image through a process of multiscale retinex, parameter adjustment, bilateral filtering, and total variation denoising filtering. Finally, it renders haze-free video according to the haze image model. The main advantage of the proposed approach is its speed as this approach adopts a "universal strategy" that applies the same atmospheric light and a universal pseudo-transmission map to a series of video frames. Experiments demonstrate that our method produces visually pleasing defogging results and tends to preserve main details better than previous techniques. A comparative study and quantitative evaluation show the efficiency of the proposed method.

Universal strategy for surveillance video defogging

The security of restricted areas such as borders or buffer zones is of utmost importance; in particular with the worldwide increase of military conflicts, illegal immigrants, and terrorism over the past decade. Monitoring such areas rely currently on technology and man power, however automatic monitoring has been advancing in order to avoid potential human errors that can be caused by different reasons. This paper introduces an automatic moving object detection, extraction and recognition system (aMODERs), which uses image processing to detect and extract moving objects within a restricted area, and a neural network to recognize the extracted object. The proposed system monitors movement by humans, animals or vehicles across a secured zone. Experimental results indicate that (aMODERs) provides a simple, efficient and fast solution to the problem of detecting, extracting and recognizing moving objects within one system.

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Automatic Detection, Extraction and Recognition of Moving Objects

Intelligent visual surveillance system can work normally under clear weather. But under bad weather, especially in foggy days, it can not detect moving objects accurately due to low scene visibility. Our research aims to resolve this problem. This paper presents a novel method for moving object detection in foggy days. Firstly, surveillance video under foggy weather is defogged, leveraging a physics-based image restoration approach. Secondly, we exploit a novel background maintenance algorithm based on the Unscented Kalman Filter(UKF) to subtract the background from the defogged video. Finally, moving objects are segmented by background differencing. Evaluations are performed to verify the effectiveness and practicality of this approach. Experimental results show that our method can be applied in real time surveillance systems.

Ziqiang Wang

Papers

A Novel Method for Moving Object Detection in Foggy Day