Autoencoder-based Image Denoiser Suitable for Image of Numbers with High Potential for Hardware Implementation

Abstract

The algorithms for processing images and videos are currently essential for many applications. Many of these applications are specified for processing and analyzing images of numbers, such as smart meter reading, automated document processing, and processing of vehicles and license plate images in traffic monitoring and analysis. Consequently, eliminating noise is frequently used as a pre-processing step to improve subsequent analysis and processing outcomes. In this context, this manuscript proposes using artificial intelligence-based methods to increase the efficiency of the image-denoising process. However, the computational demands of these algorithms necessitate careful consideration of the hardware on which they are implemented. Therefore, this paper proposes using the simple autoencoder approach and evaluates its efficiency compared to the conventional methods. This unsupervised model is trained to identify and remove impulse noise from digital images by replacing some pixels with others from the outer dataset that can clarify the whole image more. The model was trained using handwritten numbers, MNIST, and data set size in the first trial and the FER2013 dataset in the second. The model is superior in the case of the simple dataset. Two versions of autoencoders are considered, the first with three layers and the second with five. The Traditional denoising methods are investigated for comparison purposes. The four conventional filtering procedures, AMF, DBMF, ADBMF, and MDBUTMF, are implemented using the MATLAB simulation environment, and the results are reported and compared with the proposed methodology. The results show that the proposed artificial intelligence-based method significantly outperforms the traditional methods regarding processing efficiency and the resulting image quality. Moreover, the computational intensity for the proposed methodology is chosen as a metric for evaluating the algorithm compliance for the hardware implementation compared to the other Artificial Intelligence (AI)-based denoising algorithms. The suggested technique has minor processing and training time compared to the other AI-based methods with adequate quality in case the images of numbers usually do not contain many details, making it more convenient for hardware implementation.