AIDEDNet:anti-interference and detail enhancement dehazing network for real-world scenes

The haze phenomenon seriously interferes the image acquisition and reduces image quality.Due to many uncertain factors,dehazing is typically a challenge in image processing.The most existing deep learning-based dehazing approaches apply the atmospheric scattering model(ASM)or a similar physical model,which originally comes from traditional dehazing methods.However,the data set trained in deep learning does not match well this model for three reasons.Firstly,the atmospheric illumination in ASM is obtained from prior experience,which is not accurate for dehazing real-scene.Secondly,it is difficult to get the depth of outdoor scenes for ASM.Thirdly,the haze is a complex natural phenomenon,and it is difficult to find an accurate physical model and related parameters to describe this phenomenon.In this paper,we propose a black box method,in which the haze is considered an image quality problem without using any physical model such as ASM.Analytically,we propose a novel dehazing equation to combine two mechanisms:interference item and detail enhancement item.The interference item estimates the haze information for dehazing the image,and then the detail enhancement item can repair and enhance the details of the dehazed image.Based on the new equation,we design an antiinterference and detail enhancement dehazing network(AIDEDNet),which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training.Specifically,we propose a new way to construct a haze patch on the flight of network training.The patch is randomly selected from the input images and the thickness of haze is also randomly set.Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.

A combined image matching method for Chinese optical satellite imagery

Image matching is one of the key technologies for digital Earth.This paper presents a combined image matching method for Chinese satellite images.This method includes the following four steps:(1)a modified Wallis-type filter is proposed to determine parameters adaptively while avoiding over-enhancement;(2)a mismatch detection procedure based on a global-local strategy is introduced to remove outliers generated by the Scale-invariant feature transform algorithm,and geometric orientation with bundle block adjustment is employed to compensate for the systematic errors of the position and attitude observations;(3)we design a novel similarity measure(distance,angle and the Normalized Cross-Correlation similarities,DANCC)which considers geometric similarity and textural similarity;and(4)we introduce a hierarchical matching strategy to refine the matching result level by level.Four typical image pairs acquired from Mapping Satellite-1,ZY-102C,ZY-3 and GeoEye-1,respectively,are used for experimental analysis.A comparison with the two current main matching algorithms for satellite imagery confirms that the proposed method is capable of producing reliable and accurate matching results on different terrains from not only Chinese satellite images,but also foreign satellite images.