Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the image denoising problem, where zeromean white and homogeneous Gaussian additive noise should be removed from a given image. The approach taken is based on sparse and redundant representations over a trained dictionary. The proposed algorithm denoises the image, while simultaneously trainining a dictionary on its (corrupted) content using the K-SVD algorithm. As the dictionary training algorithm is limited in handling small image patches,  extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image.  show how such Bayesian treatment leads to a simple and effective denoising algorithm, with state-of-the-art performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.
['Michael Elad', 'Michal Aharon']
I am writing a research paper on a idea Image Denoising Via Learned Dictionaries and Sparse representation
[{"from": "human", "value": "I am writing a research paper on a idea Image Denoising Via Learned Dictionaries and Sparse representation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the image denoising problem, where zeromean white and homogeneous Gaussian additive noise should be removed from a given image. The approach taken is based on sparse and redundant representations over a trained dictionary. The proposed algorithm denoises the image, while simultaneously trainining a dictionary on its (corrupted) content using the K-SVD algorithm. As the dictionary training algorithm is limited in handling small image patches,  extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image.  show how such Bayesian treatment leads to a simple and effective denoising algorithm, with state-of-the-art performance, equivalent and sometimes surpassing recently published leading alternative denoising methods."}]