Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Depth resolution enhancement aims to recover a high quality depth map from one or multiple low-resolution depth input(s) with missing pixels. While a registered high-resolution intensity image is often utilized to assist, little attention has been paid to the circumstances when there is only one pair of low-resolution depth map and aligned intensity image available. ,  propose a novel resolution enhancement approach that targets at improving the quality of both the input depth map and the low-resolution RGB image. By exploiting the statistical dependency beten the input pairs, a label matrix is generated utilizing the support vector machine classifier. Guided by the constructed label matrix and the aligned intensity image, the missing values in the depth map are ll predicted in a manner consistent with the embedded structure. After that, the completed depth map and the intensity image are super-resolved through a set of regression models trained via external exemplars. Extensive experiments demonstrate that our framework is effective with satisfying performance.
['Yingli Tian', 'Yang Xian']
I am writing a research paper on a idea Resolution enhancement in single depth map and aligned image
[{"from": "human", "value": "I am writing a research paper on a idea Resolution enhancement in single depth map and aligned image"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Depth resolution enhancement aims to recover a high quality depth map from one or multiple low-resolution depth input(s) with missing pixels. While a registered high-resolution intensity image is often utilized to assist, little attention has been paid to the circumstances when there is only one pair of low-resolution depth map and aligned intensity image available. ,  propose a novel resolution enhancement approach that targets at improving the quality of both the input depth map and the low-resolution RGB image. By exploiting the statistical dependency beten the input pairs, a label matrix is generated utilizing the support vector machine classifier. Guided by the constructed label matrix and the aligned intensity image, the missing values in the depth map are ll predicted in a manner consistent with the embedded structure. After that, the completed depth map and the intensity image are super-resolved through a set of regression models trained via external exemplars. Extensive experiments demonstrate that our framework is effective with satisfying performance."}]