Missing Content Restoration and Ghosting Suppression Network for High Dynamic Range Imaging
High dynamic range(HDR)imaging based on multi-exposure fusion aims to generate high-quality HDR images by integrating the information from multiple low dynamic range(LDR)images.However,HDR imaging is faced with two major challenges,ghosting artifact suppression in motion regions and lost information restoration in over-saturated areas.To comprehensively address the challenges of restoring missing content from reference images and suppressing ghosting artifacts in motion regions,a missing content restoration and ghosting suppression network for high dynamic range imaging is proposed in this paper.In terms of content restoration,a predictive filtering-based content restoration block is introduced.The filtering kernel predicted by the content restoration block is employed to filter reference image features,integrating key information from both reference images and non-reference images to provide richer information for effective reconstruction of missing content.To suppress ghosting artifacts in motion regions and fully exploit complementary information from non-reference images,deformable convolutions are introduced to align features from non-reference images with those from the reference image.Additionally,to enhance the HDR image reconstruction capability of the network,a three-branch image reconstruction module is constructed,including a main branch and two auxiliary branches.The auxiliary branches assist the main branch with better preserved details during the generation of HDR results.Experimental results demonstrate superior performance of the proposed network.
High Dynamic Range ImagingGhosting SuppressionPredictive FilteringContent Res-toration
万方数据
维普
