Analyzing the Performance of Image Denoising Techniques

Abstract

With the increasing use of digital images, there is a growing demand for finer-tuned images that will improve the quality of what is being captured. Images captured by modern cameras are noisier, which reduces their quality. It is therefore imperative to reduce the amount of noise in these images, as well as the sharpness of the edges, corners, and other details of the image without reducing the image quality. Image noise is one of the main concerns on digital cameras. It has been found that there are a variety of techniques that can be used to reduce noise in images, each of which has different advantages and disadvantages. However, there is still a challenge and concern associated with image denoising since, when noise is removed from an image, the image is likely to appear blurred. The purpose of this paper is to discuss the characteristics of different types of noise in an image, as well as some of the different types of denoising techniques for the right way capture . The objective of this paper is to introduce various denoising techniques that can remove noise from images while maintaining a high level of image quality. Our study also utilized bilateral and nonlocal means filtering techniques, as well as total variability denoising to demonstrate the denoising in a noisy image. The numerical data in pixels in an image is typically changed by digital image filters (convolution kernels). Filters can cause artifacts in an image if they are not used carefully, leading to a misinterpretation of the data. We have ethically applied the filtering techniques in our experiment.