Wencheng Wang

Fast Image Dehazing Method Based on Linear Transformation

Images captured in hazy or foggy weather conditions are seriously degraded by the scattering of atmospheric particles, which directly influences the performance of outdoor computer vision systems. In this paper, a fast algorithm for single image dehazing is proposed based on linear transformation by assuming that a linear relationship exists in the minimum channel between the hazy image and the haze-free image. First, the principle of linear transformation is analyzed. Accordingly, the method of estimating a medium transmission map is detailed and the weakening strategies are introduced to solve the problem of the brightest areas of distortion. To accurately estimate the atmospheric light, an additional channel method is proposed based on quad-tree subdivision. In this method, average grays and gradients in the region are employed as assessment criteria. Finally, the haze-free image is obtained using the atmospheric scattering model. Numerous experimental results show that this algorithm can clearly and naturally recover the image, especially at the edges of sudden changes in the depth of field. It can, thus, achieve a good effect for single image dehazing. Furthermore, the algorithmic time complexity is a linear function of the image size. This has obvious advantages in running time by guaranteeing a balance between the running speed and the processing effect.

Recent advances in image dehazing

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which reduces the contrast, changes the color, and makes the object features difficult to identify by human vision and by some outdoor computer vision systems. Therefore image dehazing is an important issue and has been widely researched in the field of computer vision. The role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing. This paper reviews the main techniques of image dehazing that have been developed over the past decade. Firstly, we innovatively divide a number of approaches into three categories: image enhancement based methods, image fusion based methods and image restoration based methods. All methods are analyzed and corresponding sub-categories are introduced according to principles and characteristics. Various quality evaluation methods are then described, sorted and discussed in detail. Finally, research progress is summarized and future research directions are suggested.

An efficient method for image dehazing

Hazy images hinder image understanding in many applications such as autonomous vehicle. In this paper, we propose an efficient method to improve image quality of hazy images. Our method estimates the transmission function based on a linear model that allows efficient computation and employs quadtree to search for a region that best represents the scatter of airlight. Experiments were conducted using publicly available images. It is demonstrated that our proposed method achieved comparable results to the state-of-the-art ones. In the estimation of sunlight radiance, the quadtree that integrates local brightness and gradient as well as spatial constraint provide a robust means to identify region of sky. Most significantly, our proposed method greatly improved the efficiency. When dealing with moderate and large size image, the improvement could be more than thirty-fold.

Automatic pipes counting system based on digital image processing technology

In order to improve the efficiency and accuracy of pipes counting, a system was designed based on the machine vision and image processing technology in this paper, which can automatically count the number of the pipes online. Firstly, the image was captured by CCD and input into computer for processing. Then, a kind of gray image preprocessing methods such as denoising, binary conversion, mathematical morphology have been conducted. Finally, an accurate counting strategy of pipe image is adopted based on 8-neighborhood region labeling algorithm. Experiment results show that this method is convenient, and it is effective to deal with the conglutination problems and enhances the accuracy of the pipe automatic counting.