Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The denoising of a natural image corrupted by Gaussian noise is a classical problem in signal or image processing. Donoho and his coworkers at Stanford pioneered a wavelet denoising scheme by thresholding the wavelet coefficients arising from the standard discrete wavelet transform. This work has been widely used in science and engineering applications. Hover, this denoising scheme tends to kill too many wavelet coefficients that might contain useful image information. ,  propose one wavelet image thresholding scheme by incorporating neighbouring coefficients, namely NeighShrink. This approach is valid because a large wavelet coefficient will probably have large wavelet coefficients as its neighbours. Experimental results show that NeighShrink is better than the Wiener filter and the conventional wavelet denoising approaches: VisuShrink and SUREShrink.  also investigate different neighbourhood sizes and find that a size of 3/spl times/3 is the best among all window sizes.
['Guangyi Chen', 'Tien D. Bui', 'Adam Krzyzak']
I am writing a research paper on a idea Image denoising using neighbouring wavelet coefficients
[{"from": "human", "value": "I am writing a research paper on a idea Image denoising using neighbouring wavelet coefficients"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The denoising of a natural image corrupted by Gaussian noise is a classical problem in signal or image processing. Donoho and his coworkers at Stanford pioneered a wavelet denoising scheme by thresholding the wavelet coefficients arising from the standard discrete wavelet transform. This work has been widely used in science and engineering applications. Hover, this denoising scheme tends to kill too many wavelet coefficients that might contain useful image information. ,  propose one wavelet image thresholding scheme by incorporating neighbouring coefficients, namely NeighShrink. This approach is valid because a large wavelet coefficient will probably have large wavelet coefficients as its neighbours. Experimental results show that NeighShrink is better than the Wiener filter and the conventional wavelet denoising approaches: VisuShrink and SUREShrink.  also investigate different neighbourhood sizes and find that a size of 3/spl times/3 is the best among all window sizes."}]