Consensus image method for unknown noise removal

Noise removal has been, and it is nowadays, an important task in computer vision. Usually, it is a previous task preceding other tasks, as segmentation or reconstruction. However, for most existing denoising algorithms the noise model has to be known in advance. In this paper, we introduce a new approach based on consensus to deal with unknown noise models. To do this, different filtered images are obtained, then combined using multifuzzy sets and averaging aggregation functions. The final decision is made by using a penalty function to deliver the compromised image. Results show that this approach is consistent and provides a good compromise between filters.This work is supported by the European Commission under Contract No. 238819 (MIBISOC Marie Curie ITN). H. Bustince was supported by Project TIN 2010-15055 of the Spanish Ministry of Science

Hybrid filtering technique to remove noise of high density from digital images

Noise removal is one of the greatest challenges among the researchers, noise removal algorithms vary with the application areas and the type of images and noises. The work proposes a novel hybrid filter which is capable of predicting the best filter for every pixel using neural network and choose the best technique to remove noise with 3x3 mask operation. Proposed algorithm first train the neural network for various filters like mean, median, mode, geometrical mean, arithmetic mean and will use to remove noise later on. Later, the proposed method is compared with the existing techniques using the parameters MAE, PSNR, MSE and IEF. The experimental result shows that proposed method gives better performance in comparison with MF, AMF and other existing noise removal algorithms and improves the values of various parameter

An Impressive Method to Get Better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) Values Using Stationary Wavelet Transform (SWT)

Impulse noise in images is present because of bit errors in transmission or introduced during the signal acquisition stage. There are two types of impulse noise, they are salt and pepper noise and random valued noise. In our proposed method, first we apply the Stationary wavelet transform for noise added image. It will separate into four bands like LL, LH, HL and HH. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 02019;s and 2552019;s are present in the selected window and when all the pixel values are 02019;s and 2552019;s then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard median filter (MF), decision based algorithm (DBA). The proposed method performs well in removing low to medium density impulse noise with detail preservation up to a noise density of 70% and it gives better Peak signal-to-noise ratio (PSNR) and mean square error (MSE) values