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

Noise Suppression in Images by Median Filter

A new and efficient algorithm for high-density salt and pepper noise removal in images and videos is proposed. In the transmission of images over channels, images are corrupted by salt and pepper noise, due to faulty communications. Salt and Pepper noise is also referred to as Impulse noise. The objective of filtering is to remove the impulses so that the noise free image is fully recovered with minimum signal distortion. Noise removal can be achieved, by using a number of existing linear filtering techniques. We will deal with the images corrupted by salt-and-pepper noise in which the noisy pixels can take only the maximum or minimum values (i.e. 0 or 255 for 8-bit grayscale images)

Improvement Of Quantized Adaptive Switching Median Filter For Impulse Noise Reduction In Grayscale Digital Image

In this dissertation, an improvement to Quantized Adaptive Switching Median filter (QSAM) has been done, to make it more efficient in reducing high density fixedvalued impulse noise from grayscale digital images. QSAM uses the switching approach, where it has noise detection and noise cancellation blocks. This approach minimizes unwanted changes from the filtering process. QSAM also uses adaptive approach, where the filter size is adaptable to the local noise content. QSAM has two main stages. In the first stage, the image is filtered using the filtering window with quantized size. In the second stage, the image is filtered using adaptive window size. Improvement to QSAM has been carried out by replacing the formula used to restore the corrupted pixel. Instead of using the local median value, this proposed method uses the average of the local mean and local median values. Experimental results using three standard grayscale images of size 512 512 pixels show that the proposed method has the ability to restore the corrupted images even up to 95% of corruption. As compared to other thirteen median filters, the proposed method had the lowest Mean Square Error (MSE) and produce outputs with the best visual appearance

A Survey of Non-Linear Filtering Techniques For Image Noise Removal

Image is captured or noninheritable by any image capturing device like camera or scanner and then it is stored in the mass storage of the computer system. In many of these applications the existence of impulsive noise among the noninheritable pictures is one altogether common problems. This noise is characterized by spots on the image and is usually related to the innate image because of errors in image sensors and information transmission. Now-a-days there are numerous strategies that are offered to remove noise from digital images. Most of the novel methodology includes 2 stages: the primary stage is to find the noise within the image and the second stage is to eliminate the noise from the image. This paper explores the varied novel methods for the removal of noise from the digital images. The distinctive feature of the all the described filters is that offers well line, edge and detail preservation performance while, at the constant time, effectively removing noise from the input image. In later section, we present a short introduction for various strategies for noise reduction in digital images

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 Image Enhancement Approach to Achieve High Speed Using Adaptive Modified Bilateral Filter for Satellite Images Using FPGA

For real time application scenarios of image processing, satellite imaginary has grown more interest by researches due to the informative nature of image. Satellite images are captured using high quality cameras. These images are captured from space using on-board cameras. Wrong ISO setting, camera vibrations or wrong sensory setting causes noise. The degraded image can cause less efficient results during visual perception which is a challenging issue for researchers. Another reason is that noise corrupts the image during acquisition, transmission, interference or dust particles on the scanner screen of image from satellite to the earth stations. If quality degraded images are used for further processing then it may result in wrong information extraction. In order to cater this issue, image filtering or denoising approach is required. Since remote sensing images are captured from space using on-board camera which requires high speed operating device which can provide better reconstruction quality by utilizing lesser power consumption. Recently various approaches have been proposed for image filtering. Key challenges with these approaches are reconstruction quality, operating speed, image quality by preserving information at edges on image. Proposed approach is named as modified bilateral filter. In this approach bilateral filter and kernel schemes are combined. In order to overcome the drawbacks, modified bilateral filtering by using FPGA to perform the parallelism process for denoising is implemented