Design and Development of a Speckle Reduction Method for Enhanced Image Quality

Specal noise medical ultrasound makes images very little, making it difficult to make accurate diagnosis and analysis. This task offers a new way to improve images that combine wavelength threshing and particle herd optimization (PSO) to effectively reduce special noise, keeping important aspects of the image. The main goal is to create and improve a despecling method based on thresholding by choosing the best thresholding parameters "β" automatically to achieve the highest age headx (EPI). The suggested strategy has been tested on both synthetic and real -life liver ultrasonography dataset. First, synthetic images are given spotted noise with separate versions (0.01–0.2). Again, the logarithmic change and three-tier wavelet decomposition are performed with simulat 8. This study uses PSO to obtain the best "β" value using EPIs as a target function. Then, this study uses objective measures such as the average class error (MSE), signal-to-show ratio (SNR), peak signal-to-show ratio (PSNR), and EPI. The results suggest that the suggested method works better than specific depressing techniques including exponential thresholding, veneer filtering, mean filtering, SRAD, and standard wavelet danoizing. The visual comparison of both synthetic and clinical ultrasound shows that this method does a better job of getting rid of noise and keeping the edges sharp. The study also suggests a piece-defined ideal β value based on noise variance, which makes sufficient flexible to work with different amounts of noise. This task makes a powerful, flexible structure to reduce special noise. Its real -time medical image is important implications for growth systems, which can improve more accurate and automatic clinical analysis in a wide variety of healthcare contexts.