An Improved Image Denoising Approach Using Optimized Variance-Stabilizing Transformations


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Abstract


Image denoising plays a very important process in image processing. Many researchers would study about the various noise removal approaches used for fluorescence images. The fluorescence images mainly contain Poisson noise. The process of denoising is the image is gaussianized first then the resulting image is given as an input to the OWT SURELET to remove the Gaussian white noise. The OWT SURELET is one of the conventional denoising algorithms used to remove the noises. Then to attain an exact signal an inverse transform is applied to the denoised signal. Difficulties may arise to choose the inverse transformation for fluorescence images because the bias error may occur if the non linear forward transform is applied.  To overcome these difficulties a study is made on the proposed Anscombe transformation and OWT-SURELET suitable for fluorescence images. The proposed OWT-SURELET is compared with BLS_GSM strategy and the results are discussed. Experimental result shows that the proposed system is more efficient than the existing system, the results are tested using ISNR changes with the denoising algorithms and the inverse transforms.
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Keywords


Clustering Analysis; Ontology; R&D; Text Mining; Knowledge Based Agent; Fuzzy SOM; NRGA Algorithm

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References


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