Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Analysis and interpretation of an image which was acquired by a nonideal imaging system is the key problem in many application areas. The observed image is usually corrupted by blurring, spatial degradations, and random noise. Classical methods like blind deconvolution try to estimate the blur parameters and to restore the image.  propose an alternative approach.  derive the features for image representation which are invariant with respect to blur regardless of the degradation PSF provided that it is centrally symmetric. As  prove in the paper, there exist two classes of such features: the first one in the spatial domain and the second one in the frequency domain.  also derive so-called combined invariants, which are invariant to composite geometric and blur degradations. Knowing these features,  can recognize objects in the degraded scene without any restoration.
['Jan Flusser', 'Tomáš Suk']
I am writing a research paper on a idea Degraded image analysis: an invariant approach
[{"from": "human", "value": "I am writing a research paper on a idea Degraded image analysis: an invariant approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Analysis and interpretation of an image which was acquired by a nonideal imaging system is the key problem in many application areas. The observed image is usually corrupted by blurring, spatial degradations, and random noise. Classical methods like blind deconvolution try to estimate the blur parameters and to restore the image.  propose an alternative approach.  derive the features for image representation which are invariant with respect to blur regardless of the degradation PSF provided that it is centrally symmetric. As  prove in the paper, there exist two classes of such features: the first one in the spatial domain and the second one in the frequency domain.  also derive so-called combined invariants, which are invariant to composite geometric and blur degradations. Knowing these features,  can recognize objects in the degraded scene without any restoration."}]