No abstract

In this paper, we proposed a guidance correlation pixel Sampling and aggregation image denoising for maritime Unmanned Aerial Vehicles(UAVs) photography, providing a reliable data base for maritime reconnaissance work. The overall step is mainly composed of two parts, pixel sampling and pixel aggregation. To improve the content correlation of sampled pixels, we propose a guided sampling scheme based on the basic estimated map and extend this algorithm to the restoration of maritime UAVs image. Finally, a UAVs image denoising system is shown. Our experimental results show that the proposed algorithm can effectively remove noise and achieves 32.98dB and 31.83dB in Set12 and BSD68 datasets with less and image distortion. In the actual scene, the PSNR of our denoising algorithm has reached 35.33dB, meets the basic needs of practical vision and follow-up research.

In this paper, we propose an end-to-end two-branch feature attention network. The network is mainly used for single image dehazing. The network consists of two branches, we call it CAA-Net: 1) A U-NET network composed of different-level feature fusion based on attention (FEPA) structure and residual dense block (RDB). In order to make full use of all the hierarchical features of the image, we use RDB. RDB contains dense connected layers and local feature fusion with local residual learning.We also propose a structure which called FEPA.FEPA structure could retain the information of shallow layer and transfer it to the deep layer.FEPA is composed of serveral feature attention modules (FPA). FPA combines local residual learning with channel attention mechanism and pixel attention mechanism, and could extract features from different channels and image pixels. 2) A network composed of several different levels of FEPA structures.The network could make feature weights learn from FPA adaptively, and give more weight to important features. The final output result of CAA-Net is the combination of all branch prediction results. Experimental results show that the CAA -Net proposed by us surpasses the most advanced algorithms before for single image dehazing. key words: End-to-end,two-branch feature attention network,single image dehazing,residual dense block,all branch prediction results.