Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: First,  present a novel cost aggregation method for stereo matching that uses two edge-sensitive shape-adaptive support windows per pixel region; one following the horizontal edges in the image, the other the vertical edges. Their combination defines the final aggregation window shape that closely follows all object edges and thereby achieves increased hypothesis confidence. Second,  present a novel iterative disparity refinement process and apply it to the initially estimated disparity map. The process consists of four rigorously defined and lightight modules that can be iterated multiple times: a disparity cross check, bitwise fast voting, invalid disparity handling, and median filtering.  demonstrate that our iterative refinement has a large effect on the overall quality, resulting in smooth disparity maps with sharp object edges, especially around occluded areas. It can be applied to any stereo matching algorithm and tends to converge to a final solution. Finally,  perform a quantitative evaluation on various Middlebury datasets, showing an increase in quality of over several dB PSNR compared with their ground truth. Our whole disparity estimation algorithm supports efficient GPU implementation to facilitate scalability and real-time performance.
['Maarten Dumont', 'Patrik Goorts', 'Steven Maesen', 'Gauthier Lafruit', 'Philippe Bekaert']
I am writing a research paper on a idea Real-Time Edge-Sensitive Local Stereo Matching with Iterative Disparity Refinement
[{"from": "human", "value": "I am writing a research paper on a idea Real-Time Edge-Sensitive Local Stereo Matching with Iterative Disparity Refinement"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: First,  present a novel cost aggregation method for stereo matching that uses two edge-sensitive shape-adaptive support windows per pixel region; one following the horizontal edges in the image, the other the vertical edges. Their combination defines the final aggregation window shape that closely follows all object edges and thereby achieves increased hypothesis confidence. Second,  present a novel iterative disparity refinement process and apply it to the initially estimated disparity map. The process consists of four rigorously defined and lightight modules that can be iterated multiple times: a disparity cross check, bitwise fast voting, invalid disparity handling, and median filtering.  demonstrate that our iterative refinement has a large effect on the overall quality, resulting in smooth disparity maps with sharp object edges, especially around occluded areas. It can be applied to any stereo matching algorithm and tends to converge to a final solution. Finally,  perform a quantitative evaluation on various Middlebury datasets, showing an increase in quality of over several dB PSNR compared with their ground truth. Our whole disparity estimation algorithm supports efficient GPU implementation to facilitate scalability and real-time performance."}]