Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses the problem of generating a super-resolution (SR) image from a single low-resolution input image.  approach this problem from the perspective of compressed sensing. The low-resolution image is vied as downsampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signal-atoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the downsampled signal.  will demonstrate the effectiveness of sparsity as a prior for regularizing the otherwise ill-posed super-resolution problem.  further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high-resolution image is competitive or even superior in quality to images produced by other SR methods.
['Jianchao Yang', 'John Wright', 'Thomas S. Huang', 'Yi Ma']
I am writing a research paper on a idea Image super-resolution as sparse representation of raw image patches
[{"from": "human", "value": "I am writing a research paper on a idea Image super-resolution as sparse representation of raw image patches"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses the problem of generating a super-resolution (SR) image from a single low-resolution input image.  approach this problem from the perspective of compressed sensing. The low-resolution image is vied as downsampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signal-atoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the downsampled signal.  will demonstrate the effectiveness of sparsity as a prior for regularizing the otherwise ill-posed super-resolution problem.  further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high-resolution image is competitive or even superior in quality to images produced by other SR methods."}]