Combined with two different types of image dehazing strategies based on image enhancement and atmospheric physical model, respectively, a novel method for gray-scale image dehazing is proposed in this paper. For image-enhancement-based strategy, the characteristics of its simplicity, effectiveness, and no color distortion are preserved, and the common guided image filter is modified to match the application of image enhancement. Through wavelet decomposition, the high frequency boundary of original image is preserved in advance. Moreover, the process of image dehazing can be guided by the image of scene depth proportion directly estimated from the original gray-scale image. Our method has the advantages of brightness consistency and no distortion over the state-of-the-art methods based on atmospheric physical model. Particularly, our method overcomes the essential shortcoming of the abovementioned methods that are mainly working for color image. Meanwhile, an image of scene depth proportion is acquired as a byproduct of image dehazing.

No abstract

Mask of damage region is the pretreatment step of the image inpainting, which plays a key role in the ultimate effect. However, state-of-the-art methods have attached significance to the inpainting model, and the mask of damage region is usually selected manually or by the conventional threshold-based method. Since manual method is time-consuming and the threshold-based method does not have the same precision for different images, we herein report a new method for automatically constructing the precise mask by the joint filtering of guided filtering andL0smoothing. It can accurately locate the boundary of damaged region in order to effectively segment the damage region and then greatly improves the ultimate effect of image inpainting. The experimental results show that the proposed method is superior to state-of-the-art methods in the step of constructing inpainting mask, especially for the damaged region with inconspicuous boundary.

No abstract

No abstract