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Degraded Image Enhancement through Double Density Dual Tree Discrete Wavelet Transform
Background/Objectives: Denoising is the first pre-processing step in image processing. Image denoising is the removal of noise from the corrupted image without deleting the useful information. Methods/Statistical Analysis: Wavelet transform is a main tool for image processing applications in modern existence. In this paper Double Density Dual Tree Discrete Wavelet Transform is used and investigated for image denoising. The proposed techniques give the better performance when comparing other two wavelet techniques. Findings: Images are considered for the analysis purpose and the performance is compare with other two wavelet transform discussed in this paper. Peak Signal to Noise Ratio values and Root Means Square error are calculated in all the three wavelet techniques for denoised images and the performance has evaluated. Applications/Improvements: The reduced RMSE and increased PSNR value resultant shows the good visual perception of the image which is used to image analysis.
Denoising, Discrete Wavelet Transform (DWT), Image processing, Wavelet Transform.
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