Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Hyper-spectral image fusion has been a hot topic in medical imaging and remote sensing.  proposes a Bayesian fusion model which combines the panchromatic (PAN) image and the low spatial resolution hyper-spectral (HS) image under the same framework. Sparsity constraint is introduced as double "spike-and-slab" priors, and anisotropic Gaussian noise is adopted for accuracy. To achieve reduction in computational complexity,  turn the anisotropic Gaussian distribution into isotropic one with modified linear transformation and propose a variational Bayesian expectation maximization (EM) algorithm to calculate the result. Experiment results show that our solution can achieve comparable performance in pan-sharpening to other state-of-art algorithms while largely reducing the computational complexity.
['Baihong Lin', 'Xiaoming Tao', 'Shaoyang Li', 'Linhao Dong', 'Jianhua Lu']
I am writing a research paper on a idea Variational Bayesian image fusion based on combined sparse representations
[{"from": "human", "value": "I am writing a research paper on a idea Variational Bayesian image fusion based on combined sparse representations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Hyper-spectral image fusion has been a hot topic in medical imaging and remote sensing.  proposes a Bayesian fusion model which combines the panchromatic (PAN) image and the low spatial resolution hyper-spectral (HS) image under the same framework. Sparsity constraint is introduced as double \"spike-and-slab\" priors, and anisotropic Gaussian noise is adopted for accuracy. To achieve reduction in computational complexity,  turn the anisotropic Gaussian distribution into isotropic one with modified linear transformation and propose a variational Bayesian expectation maximization (EM) algorithm to calculate the result. Experiment results show that our solution can achieve comparable performance in pan-sharpening to other state-of-art algorithms while largely reducing the computational complexity."}]