Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

With increasingly more smart cameras deployed in infrastructure and commercial buildings,3D reconstruction can quickly obtain cities’information and improve the efficiency of government services.Images collected in outdoor hazy environments are prone to color distortion and low contrast;thus,the desired visual effect cannot be achieved and the difficulty of target detection is increased.Artificial intelligence(AI)solutions provide great help for dehazy images,which can automatically identify patterns or monitor the environment.Therefore,we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning.First,we propose a fine transmission image deep convolutional regression network(FT-DCRN)dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image.The DCRN is used to obtain the coarse transmission image,which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network.The fine transmission image is obtained by refining the coarse transmission image using a guided filter.The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image.Second,we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction.An advanced relaxed iterative fine matching based on the structure from motion(ARI-SFM)algorithm is proposed.The ARISFM algorithm,which obtains the fine matching corner pairs and reduces the number of iterations,establishes an accurate one-to-one matching corner relationship.The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms.In addition,the ARI-SFM algorithm guarantees the precision and improves the efficiency.

Seismic fine imaging and its significance for natural gas hydrate exploration in the Shenhu Test Area, South China Sea

Shenhu area in South China Sea includes extensive collapse and diapir structures,forming high-angle faults and vertical fracture system,which functions as a fluid migration channel for gas hydrate formation.In order to improve the imaging precision of natural gas hydrate in this area,especially for fault and fracture structures,the present work propose a velocity stitching technique that accelerates effectively the convergence of the shallow seafloor,indicating seafloor horizon interpretation and the initial interval velocity for model building.In the depth domain,pre-stack depth migration and residual curvature are built into the model based on high-precision grid-tomography velocity inversion,after several rounds of tomographic iterations,as the residual velocity field converges gradually.Test results of the Shenhu area show that the imaging precision of the fault zone is obviously improved,the fracture structures appear more clearly,the wave group characteristics significantly change for the better and the signal-to-noise ratio and resolution are improved.These improvements provide the necessary basis for the new reservoir model and field drilling risk tips,help optimize the favorable drilling target,and are crucial for the natural gas resource potential evaluation.