Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In imaging applications such as ultrasound or Synthetic Aperture Radar (SAR), the local texture features are descriptive of the types of geological formation or biological tissue at that spatial location. Therefore, on denoising these texture images, it is essential that the local texture details characterizing the geological formation or tissue type are not lost. When processing these images, the operator usually has prior knowledge of the type of textures to expect in the image. In this work, this prior knowledge is exploited to implement a spatially-adaptive and subband-adaptive wavelet threshold that denoises texture images while preserving the characteristic features in the textures. The proposed algorithm involves three stages: texture characterization, texture region identification system training, and texture region identification and denoising. In the first stage, the texture features to be preserved are characterized by the subband energies of the wavelet decomposition details at each level. Next, the energies of the characteristic subband are used as inputs to train the adaptive neural-fuzzy inference system (ANFIS) classifier for texture region identification. Finally, the texture regions are identified by the ANFIS and the subband-adaptive BayesShrink threshold is adjusted locally to obtain the proposed spatially-adaptive and subband-adaptive threshold.
['J. Li', 'S. S. Mohamed', 'M.M.A. Salama', 'George H. Freeman']
I am writing a research paper on a idea Subband-adaptive and spatially-adaptive wavelet thresholding for denoising and feature preservation of texture images
[{"from": "human", "value": "I am writing a research paper on a idea Subband-adaptive and spatially-adaptive wavelet thresholding for denoising and feature preservation of texture images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In imaging applications such as ultrasound or Synthetic Aperture Radar (SAR), the local texture features are descriptive of the types of geological formation or biological tissue at that spatial location. Therefore, on denoising these texture images, it is essential that the local texture details characterizing the geological formation or tissue type are not lost. When processing these images, the operator usually has prior knowledge of the type of textures to expect in the image. In this work, this prior knowledge is exploited to implement a spatially-adaptive and subband-adaptive wavelet threshold that denoises texture images while preserving the characteristic features in the textures. The proposed algorithm involves three stages: texture characterization, texture region identification system training, and texture region identification and denoising. In the first stage, the texture features to be preserved are characterized by the subband energies of the wavelet decomposition details at each level. Next, the energies of the characteristic subband are used as inputs to train the adaptive neural-fuzzy inference system (ANFIS) classifier for texture region identification. Finally, the texture regions are identified by the ANFIS and the subband-adaptive BayesShrink threshold is adjusted locally to obtain the proposed spatially-adaptive and subband-adaptive threshold."}]