Analysis of Weiner Filter Approximation Value Based on Performance of Metrics of Image Restoration

Image restoration is used to recover the image quality by reducing or eliminating the noise to go back to its original image. This study aims to investigate the performance of the Wiener Filter approximation value based on Mean Squared Error (MSE), Structural Similarly Index Image (SSIM), Root Mean Squared Error (RMSE) and Peak Signal Noise Ratio (PSNR) using the three (3) sample images in different dimensions and image quality with the integration of five (5) different Gaussian noise and K-filter approximation values. Based on experiment result, the Gaussian Noise value of 10 marked a good performance based on the MSE for all the sample images however for the RMSE it performs very well in Image2 and SSIM for Image3 and the PSNR is evident in Image2. Indeed, the application of the said filter depends on the quality of the given image basis for the application of suitable measurements to achieve optimal results.