This paper presents a wavelet threshold selection technique for noise filtering and comparison of different noise reduction techniques used in image denoising. Additive noise is applied to the image and different wavelet technique is used to remove the noise from the image. Finally all the different techniques are compared for PSNR, SSIM improvement and the processing time required.

Image splicing is a common manipulation which consists in copying part of an image in a second image. In this paper, we exploit the variation in noise characteristics in spliced images, caused by the difference in camera and lighting conditions during the image acquisition. The proposed method automatically gives a probability of alteration for any area of the image, using a local analysis of n...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

Image splicing is a common and widespread type of manipulation, which is defined as pasting a portion of an image onto a second image. Several forensic methods have been developed to detect splicing, using various image properties. Some of these methods exploit the noise statistics of the image to try and find discrepancies. In this paper, we propose a new counter-forensic approach to eliminate...

Introduction Signal-to-noise ratio (SNR = η/σ) is a key image quality metric but is not always straightforward to evaluate due to the difficulties in accurately estimating the noise level in an image. There are multiple approaches, using a single image, more than one image and noise-only acquisitions. Previous work has compared several methods but not included the noise-only methods. The aim of...

Noise is an important factor that influences image quality which is mainly produced in the processes of image acquirement and transmission. Noise reduction is necessary for us to do image processing and image interpretation so as to acquire useful information that we want. Because the working status of image transmitter is influenced by varied of factors, such as the environment of image acquir...

The impact of noise on image will degrade its feature. Suppression or removal of noise in 2D images would be very worth in medical image processing, satellite image processing and various other domains. Switching median filters outperform standard median filters in the removal of impulse noise due to their capability of filtering candidate noisy pixels and leaving other pixels intact. The BDND ...

The quality of biomedical image is degraded by many sources of noise such as imperfect instruments, interference from natural phenomena and data acquisition errors. In Ultrasound and MRI images, the image quality is degraded mainly by Speckle noise and Rician noise respectively. Removing noise from biomedical image is still a challenging problem for the biomedical researchers and lots of denois...

Image denoising is the manipulation of the image data to produce a visually high quality image. At present there are a variety of methods to remove noise from digital images. There are different types of filters like mean filter, median filter, bilateral filter, wiener filter etc. to remove a single type of noise such as salt and pepper noise, speckle noise, Gaussian noise etc. But if the image...