Innovative denoising of the medical image using BIOR algorithm in comparison with SYMLET algorithm

The objective of this study is to enhance the Peak Signal to Noise Ratio and reduce RMSE Root Mean Square Error in medical image denoising. This is achieved by employing an innovative denoising approach utilizing the BIOR algorithm, which is compared against the SYMLET wavelet transform method. This study investigated a medical image denoising technique with the expectation of achieving better PSNR (Peak Signal-to-Noise Ratio) and RMSE (Root Mean Squared Error) values. The researchers used a sample size of 20 images, divided equally into two groups for comparison. To determine this sample size, they likely performed a power analysis using G-Power software. The analysis considered a G-power of 0.8 (indicating a high probability of detecting an effect if it exists), a significance level (alpha) of 0.05, a desired level of certainty (confidence interval) of 95%, and an error probability (beta) of 0.2. The BIOR algorithm produces a higher PSNR value of 71.72% when compared to the PSNR in SYMLET wavelet transform of 69.69%. The BIOR wavelet transform exhibits a superior RMSE value of 0.0047 compared to the SYMLET wavelet transform’s RMSE value of 0.0076. This difference in performance is statistically significant, with a significance value of p=0.005 (p