Image Denoising Techniques - An Overview

Abstract

Image denoising is a applicable issue found in diverse image processing and computer vision problems. There are various existing methods to denoise image. The important property of a good image denoising model is that it should completely remove noise as far as possible as well as preserve edges. This paper presents a review of some major work in area of image denoising. There have been numerous published algorithms and each approach has its assumptions, advantages and limitations. After brief introduction various methods have been explained for removing noise.

In-situ Observations of a Multi-Asperity Real Contact Area on a Submicron Scale

“…A 10× magnification lens was used for the measurements, which resulted in the same lateral resolution in x and y directions (Δx = Δy = 0.334 µm) and a vertical resolution better than 0.1 nm. Additionally, median filter [42] was used to eliminate any captured "artificial asperities" due to measurement errors, which could influence the theoretical results. Fig.…”

Comparative Analysis of Filters and Wavelet Based Thresholding Methods for Image Denoising

“…Imperfect instruments, problems with data acquisition process, and interfering natural phenomena can all corrupt the data of interest. Transmission errors and compression can also introduce noise [1]. Various types of noise present in image are Gaussian noise, Salt & Pepper noise and Speckle noise.…”

Performance Analysis of Image Denoising with Wavelet Thresholding Methods for Different Levels of Decomposition

“…Generally, data sets collected by image sensors are contaminated by noise. Imperfect instruments, problems with data acquisition process, and interfering natural phenomena can all corrupt the data of interest [1]. Various types of noise present in image are Gaussian noise, Salt & Pepper noise and Speckle noise.…”