Haze removal has become an attractive topic in recent years and several dehazing methods are proposed. Dark channel prior (DCP) is one of the most effective dehazing approaches. However, when dealing with images containing large white objects, DCP often mistakes white objects for opaque haze. It will cause the airlight to be overestimated and the transmission to be underestimated, and thus the dehazing results have serious color distortion. In view of the above problem, saliency detection is introduced into haze removal to obtain better restored images in this paper. We first propose a method for reliable airlight estimation. Then, a saliency prior is presented for hazy images, which can distinguish white objects from dense haze by saliency detection. On the basis of saliency prior, both accurate airlight and a correct transmission map can be obtained from images containing large white objects, and finally these images can be restored successfully. The experimental results illustrate that our proposed method has great superiority in color recovery compared with other state-of-art methods when dealing with images containing large white objects.
Accurate region of interest (ROI) extraction is a hotspot of remote sensing image analysis. In this paper, we propose a novel ROI extraction method based on multi-scale hybrid visual saliency analysis (MHVSA) that can be divided into two sub-models: the frequency feature analysis (FFA) model and the multi-scale region aggregation (MRA) model. In the FFA sub-model, we utilize the human visual sensitivity and the Fourier transform to produce the local saliency map. In the MRA sub-model, saliency maps of various scales are generated by aggregating regions. A tree-structure graphical model is suggested to fuse saliency maps into one global saliency map. We obtain two binary masks by segmenting the local and global saliency maps and perform the logical AND operation on the two masks to acquire the final mask. Experimental results reveal that the MHVSA model provides more accurate extraction results.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.