Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem.  take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values.  show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally,  are able to improve on state-of-the-art results by integrating edge information into our model.  believe that our new approach is an excellent starting point for future developments in this field.
['Carsten Rother', 'Martin Kiefel', 'Lumin Zhang', 'Bernhard Schölkopf', 'Peter V. Gehler']
I am writing a research paper on a idea Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
[{"from": "human", "value": "I am writing a research paper on a idea Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance"}, {"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 challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem.  take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values.  show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally,  are able to improve on state-of-the-art results by integrating edge information into our model.  believe that our new approach is an excellent starting point for future developments in this field."}]