A new method of super-resolution image reconstruction is proposed,which uses a three-step-training error backpropagation neural network(BPNN)to realize the super-resolution reconstruction(SRR)of satellite image.The me...A new method of super-resolution image reconstruction is proposed,which uses a three-step-training error backpropagation neural network(BPNN)to realize the super-resolution reconstruction(SRR)of satellite image.The method is based on BPNN.First,three groups learning samples with different resolutions are obtained according to image observation model,and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN,at last,three times consecutive training are carried on the BPNN.Training samples used in each step are of higher resolution than those used in the previous steps,so the increasing weights store a great amount of information for SRR,and network performance and generalization ability are improved greatly.Simulation and generalization tests are carried on the well-trained three-step-training NN respectively,and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.展开更多
The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gra...The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps.This work uses the consistency check method to find an accurate depth map for identifying occluded pixels.The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation.The improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction algorithms.The experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and runtime.We observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain cumbersome.Considering this gain,we have created our dataset with occlu-sion using the structured lighting technique.The proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing coefficients.The experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.展开更多
彩色图像引导的深度图超分辨率重建能有效解决深度相机采集深度图时分辨率低和无效空洞的问题。然而,由于彩色图和深度图的结构不一致性,此类方法容易产生纹理转移伪影。针对此问题,提出了基于结构纹理分解的加权最小二乘(structure tex...彩色图像引导的深度图超分辨率重建能有效解决深度相机采集深度图时分辨率低和无效空洞的问题。然而,由于彩色图和深度图的结构不一致性,此类方法容易产生纹理转移伪影。针对此问题,提出了基于结构纹理分解的加权最小二乘(structure texture decomposition based weighted least square,STDWLS)深度超分辨率重建算法。该方法首先使用原深度图引导彩色图片结构纹理分解得到结构图,随后使用加权最小二乘优化框架来同时建模深度图上采样和空洞填充问题,并对有效深度值和空洞区域分别构建相应的惩罚函数。具体而言,对于有效深度值,该方法结合静态的结构图和动态更新的深度值计算引导权重;对于空洞,该方法仅使用结构图计算引导权重,并加入边缘自适应窗口来防止深度图边缘模糊。实验结果表明,STDWLS算法能够同时完成深度图上采样和空洞填充任务,有效抑制了纹理转移伪影,提高了深度图重建的精确度与表面结构相似度。展开更多
针对传统三维图像分层重构方法存在特征点匹配性能较差的问题,提出一种基于虚拟现实技术的三维图像分层重构方法。利用基于深度图像渲染(Depth Image Based Rendering,DIBR)技术对三维图像实施深度预处理,包括对三维图像进行深度映射处...针对传统三维图像分层重构方法存在特征点匹配性能较差的问题,提出一种基于虚拟现实技术的三维图像分层重构方法。利用基于深度图像渲染(Depth Image Based Rendering,DIBR)技术对三维图像实施深度预处理,包括对三维图像进行深度映射处理与深度平滑处理,减少三维图像的噪声与三维变换导致的图像空洞的数量与大小,利用固定块数法将三维图像分块,通过角点检测算法实现三维图像的角点检测。根据检测结果,通过灰度相关约束实现角点的初始匹配,基于基本矩阵,对初始匹配中存在的误匹配进行剔除,根据角点匹配结果,利用基于虚拟现实技术对三维图像进行三维分层,利用三角剖分算法实现三维图像的分层重构。实验结果表明,与传统方法相比,所提方法特征点匹配值额度高,表现出良好的特征点匹配性能,后续图片压缩质量高,为三维图像分层重构提供了新的路径。展开更多
文摘A new method of super-resolution image reconstruction is proposed,which uses a three-step-training error backpropagation neural network(BPNN)to realize the super-resolution reconstruction(SRR)of satellite image.The method is based on BPNN.First,three groups learning samples with different resolutions are obtained according to image observation model,and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN,at last,three times consecutive training are carried on the BPNN.Training samples used in each step are of higher resolution than those used in the previous steps,so the increasing weights store a great amount of information for SRR,and network performance and generalization ability are improved greatly.Simulation and generalization tests are carried on the well-trained three-step-training NN respectively,and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.
文摘The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps.This work uses the consistency check method to find an accurate depth map for identifying occluded pixels.The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation.The improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction algorithms.The experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and runtime.We observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain cumbersome.Considering this gain,we have created our dataset with occlu-sion using the structured lighting technique.The proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing coefficients.The experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.
文摘彩色图像引导的深度图超分辨率重建能有效解决深度相机采集深度图时分辨率低和无效空洞的问题。然而,由于彩色图和深度图的结构不一致性,此类方法容易产生纹理转移伪影。针对此问题,提出了基于结构纹理分解的加权最小二乘(structure texture decomposition based weighted least square,STDWLS)深度超分辨率重建算法。该方法首先使用原深度图引导彩色图片结构纹理分解得到结构图,随后使用加权最小二乘优化框架来同时建模深度图上采样和空洞填充问题,并对有效深度值和空洞区域分别构建相应的惩罚函数。具体而言,对于有效深度值,该方法结合静态的结构图和动态更新的深度值计算引导权重;对于空洞,该方法仅使用结构图计算引导权重,并加入边缘自适应窗口来防止深度图边缘模糊。实验结果表明,STDWLS算法能够同时完成深度图上采样和空洞填充任务,有效抑制了纹理转移伪影,提高了深度图重建的精确度与表面结构相似度。
文摘针对传统三维图像分层重构方法存在特征点匹配性能较差的问题,提出一种基于虚拟现实技术的三维图像分层重构方法。利用基于深度图像渲染(Depth Image Based Rendering,DIBR)技术对三维图像实施深度预处理,包括对三维图像进行深度映射处理与深度平滑处理,减少三维图像的噪声与三维变换导致的图像空洞的数量与大小,利用固定块数法将三维图像分块,通过角点检测算法实现三维图像的角点检测。根据检测结果,通过灰度相关约束实现角点的初始匹配,基于基本矩阵,对初始匹配中存在的误匹配进行剔除,根据角点匹配结果,利用基于虚拟现实技术对三维图像进行三维分层,利用三角剖分算法实现三维图像的分层重构。实验结果表明,与传统方法相比,所提方法特征点匹配值额度高,表现出良好的特征点匹配性能,后续图片压缩质量高,为三维图像分层重构提供了新的路径。
