Comparative study of denoising techniques for facial image using quality measurement

Images are very important factors in Digital Image Processing. There are many applications are available using images. And this images can be corrupted and noised by different problems and particles and for this image cannot be processed or could not give the desired result in many applications. And facial images should be preprocessed for face recognition, face detection etc. Image preprocessing may be image deblurring, noise elimination and many more. Hence image must be denoised or uncorrupted to get proper result or processing. There are many types of noises can be imposed for different reasons and also many filtering techniques can be applied to remove these noises. In our study we analyzed that which filtering techniques are suitable for which noises using different image quality measurement. The comparative study is conducted with the help of Structural Similarity Index Measurement (SSIM), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).