ANALYSIS OF THE IMPACT OF PRELIMINARY NOISY IMAGE RESTORATION BY AUTOCODER ON THE ACCURACY OF CNN CLASSIFICATION

Abstract

The paper investigates the impact of preliminary image restoration using a denoising autoencoder (DAE) on the classification accuracy of a convolutional neural network (CNN) under various types of noise. The relevance of the topic is due to the fact that in real conditions, optical images often contain distortions caused by changes in lighting, vibrations, camera movement, and other factors, which significantly complicates the task of object recognition. Traditional filters do not always provide sufficient cleaning quality and can lead to the loss of important structural features. In this regard, the use of deep neural networks, in particular autoencoders, is a promising direction for improving the robustness of computer vision algorithms to noise of various nature. The study uses the CIFAR-10 dataset and implements a two-component model: an autoencoder for preliminary cleaning and a CNN for classification. The trained autoencoder restores the image structure after exposure to Gaussian, impulse, Poisson, and speckle noise. Three series of experiments were conducted: classification of clean images, classification of noisy data without cleaning, and classification after preliminary restoration by the autoencoder. The results showed that CNN demonstrates an accuracy of 70.37% on clean data, but when noise is introduced, the accuracy drops to 30–59% depending on the type of distortion. After applying the autoencoder, classification accuracy increased to 56–60% for all types of noise, with the greatest improvement observed for Gaussian noise with high dispersion. The results confirm that using an autoencoder as a preliminary restoration step is an effective method for improving classification accuracy and reducing CNN vulnerability to noise. This approach provides better generalization and stability of the system, which is especially important for real-time applications—in particular, in dynamic systems, robotics, autonomous transport, and navigation systems, where the quality of optical data is often unstable. The study demonstrates the promise of integrating restoration and classification models into a single structure to improve the performance of computer vision systems in challenging conditions.