Image noise reduction using discrete cosine transform

Abstract

Image noise reduction using discrete cosine transform

Alekseev V.V., Shoberg A.G.

Incoming article date: 22.01.2024

This paper considers the problem of removing noise from an image based on the discrete cosine transform (DCT) algorithm. Despite its simplicity, the algorithm is still popular in image conversion. However, recently there has been a strong development of convolutional neural networks, leaving behind “traditional” signal processing methods. In this paper, we study image denoising using DCT and convolutional neural networks and creating an interpretable convolutional neural network to obtain accurate data. The basis was the Python programming language and the library for working with neural networks – PyTorch. Based on this, a neural network model was trained on The Berkeley Segmentation Dataset. Experiments have shown that the trained neural network shows results comparable to traditional image denoising algorithms.

Keywords: noise reduction, convolutional neural network, discrete cosine transform, machine learning, signal processing, Canny operator