Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a novel image-fusion framework for com- pressive imaging (CI), which is a new technology for simultaneous sampling and compressing of images based on the principle of com- pressive sensing (CS). Unlike previous fusion work operated on conventional images,  directly perform fusion on the measure- ment vectors from multiple CI sensors according to the similarity classification. First,  define a metric to evaluate the data similarity of two given CI measurement vectors and present its potential ad- vantage for classification. Second, the fusion rules for CI measure- ment vectors in different similarity types are investigated to generate a comprehensive measurement vector. Finally, the fused image is reconstructed from the combined measurements via an optimization algorithm. Simulation results demonstrate that the reconstructed im- ages in our fusion framework are visually more appealing than the fused images using other fusion rules, and our fusion method for CI significantly saves computational complexity against the fusion- after-reconstruction scheme. © 2010 SPIE and IS&T.
['Xiaoyan Luo', 'Jun Zhang', 'Jingyu Yang', 'Qionghai Dai']
I am writing a research paper on a idea Classification-based image-fusion framework for compressive imaging
[{"from": "human", "value": "I am writing a research paper on a idea Classification-based image-fusion framework for compressive imaging"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a novel image-fusion framework for com- pressive imaging (CI), which is a new technology for simultaneous sampling and compressing of images based on the principle of com- pressive sensing (CS). Unlike previous fusion work operated on conventional images,  directly perform fusion on the measure- ment vectors from multiple CI sensors according to the similarity classification. First,  define a metric to evaluate the data similarity of two given CI measurement vectors and present its potential ad- vantage for classification. Second, the fusion rules for CI measure- ment vectors in different similarity types are investigated to generate a comprehensive measurement vector. Finally, the fused image is reconstructed from the combined measurements via an optimization algorithm. Simulation results demonstrate that the reconstructed im- ages in our fusion framework are visually more appealing than the fused images using other fusion rules, and our fusion method for CI significantly saves computational complexity against the fusion- after-reconstruction scheme. \u00a9 2010 SPIE and IS&T."}]