PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY

Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz. Mean filter, Median filter and Wiener filter to remove noises existing in facial images. The noises of our interest are namely; Gaussian noise, Salt & Pepper noise, Poisson noise and Speckle noise in our study. Further, we perform a comparative study based on the parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Method (SSIM). For this research work, MATLAB R2013a on Labeled faces in Wild (lfw) database containing 120 facial images is used. Based upon the aforementioned parameters, we have attempted to analyze the performance of noise removal techniques with different types of noises. It has been observed that MSE, PSNR and SSIM for Mean filter are 44.19 with Poisson noise, 35.88 with Poisson noise and 0.197 with Gaussian noise respectively whereas for that of Median filter, these are 44.12 with Poisson noise, 46.56 with Salt & Pepper noise and 0.132 with Gaussian noise respectively. Wiener filter when contaminated with Poisson, Salt & Pepper and Gaussian noise, these parametric values are 44.52, 44.33 and 0.245 respectively. Based on these observations, we claim that the Median filtering technique works the best when contaminated with Poisson noise while the error strategy is dominant. On the other hand, Median filter also works the best with Salt & Pepper noise when Peak Signal to Noise Ratio is important. It is interesting to note that Median filter performs effectively with Gaussian noise using SSIM.