Image noise reduction by deep learning methods

Abstract

<span lang="EN-US">Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.</span>

An improved mining image segmentation with K-Means and morphology using drone dataset

“…Figure 2. Block diagram of the proposed method − Pre-processing: In this step, we will apply the necessary pre-processing steps like noise reduction [9] and contrast enhancement to improve the image quality. − K-Means clustering: We perform the algorithm on the pre-processed image (step 1) by converting the redgreen-blue (RGB) to lightness (L), red-green (A), and yellow-blue (B) (LAB) color space.…”