earticle

영어

Mixed noises are a characteristic of combined noises acting on a single carrier. Various mechanisms in recent past have been given in literature to restore images corrupted with Poisson and impulse mixed noise. This paper compares mixed noise removal techniques such as: Peer Group averaging (PGA), Vector Median Filter (VMF), Vector Direction Filter (VDF), Fuzzy Peer Group Averaging (FPGA), and Fuzzy Vector Median Filter (FVMF) on the basis of performance metrics such as Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE), Mean Square Error (MSE) and time complexity. The image size and the noise density is varied so as record these performance metrics. All the above mentioned techniques were implemented in MATLAB-11. The simulation and result shows that FVMF introduces blurring of edged but provide an output of highest PSNR value, especially for large sized images. However, for smaller images PGA provides best results of PSNR and hence a good quality of de-noised image. Also it is observed that with increase in image size the quality of the resulting image improves as the value of PSNR also increases but on increasing the impulse noise density with constant image size the image quality decreases with a constant decrease in the PSNR value.