An Efficient Image Noise Estimation Technique Using Type-2 Fuzzy Set and Genetic Algorithm

Noise estimation in digital images plays an important role in various kinds of applications such as medical imaging, video processing and communication systems. Noise density estimation from an image can help to analyze the channel properties of a communication system. Some authors have been described with a few algorithms to estimate noise present in the images in the spatial and frequency domain. The quality of a captured image through image acquisition devices like camera or sensor will not be noise free because of some parameters like circuit noise and atmosphere conditions. The main objective of this project is to evaluate the noise levels at different parts of the image with the help of a new algorithm Interval Type-2 Fuzzy Sets Filter (IVT2 FSs) and Genetic algorithm (GA). In this, we have estimated the performance in terms of the Structural Similarity Index Metric (SSIM), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Noise Variation (NV).