Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In compressed sensing,  wish to reconstruct a sparse signal $x$ from observed data $y$. In sparse coding, on the other hand,  wish to find a representation of an observed signal $y$ as a sparse linear combination, with coefficients $x$, of elements from an overcomplete dictionary. While many algorithms are competitive at both problems when $x$ is very sparse, it can be challenging to recover $x$ when it is less sparse.  present the Difference Map, which excels at sparse recovery when sparseness is lor and noise is higher. The Difference Map out-performs the state of the art with reconstruction from random measurements and natural image reconstruction via sparse coding.
['Will Landecker', 'Rick Chartrand', 'Simon DeDeo']
I am writing a research paper on a idea Robust Compressed Sensing and Sparse Coding with the Difference Map
[{"from": "human", "value": "I am writing a research paper on a idea Robust Compressed Sensing and Sparse Coding with the Difference Map"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In compressed sensing,  wish to reconstruct a sparse signal $x$ from observed data $y$. In sparse coding, on the other hand,  wish to find a representation of an observed signal $y$ as a sparse linear combination, with coefficients $x$, of elements from an overcomplete dictionary. While many algorithms are competitive at both problems when $x$ is very sparse, it can be challenging to recover $x$ when it is less sparse.  present the Difference Map, which excels at sparse recovery when sparseness is lor and noise is higher. The Difference Map out-performs the state of the art with reconstruction from random measurements and natural image reconstruction via sparse coding."}]