Zooming Into Clarity: Image Denoising Through Innovative Autoencoder Architectures

Abstract

In today’s era of increasing data complexity and pervasive noise, robust techniques for data processing, reconstruction, and denoising are crucial. Autoencoders, known for their adaptability in unsupervised learning, offer a strategic solution to these challenges. This research introduces two novel autoencoder architectures, ResoFocus and FragmentumZoom, complemented by a refined loss function. The ResoFocus Autoencoder is designed to optimize and denoise upsized images, while the FragmentumZoom Autoencoder targets small image segments to enhance denoising tasks. Empirical evaluations on diverse datasets, including CelebA and Autism Face data, demonstrate significant improvements in the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). For the CelebA dataset, the ResoFocus 4X autoencoder achieved PSNR values of 31.578, with a peak increase of 1.9 dB compared to the baseline Simple autoencoder and SSIM values of 0.898 at 1000 epochs. In the Autism Face dataset, the FragmentumZoom 4X autoencoder recorded PSNR values of 30.951, with an increase of 1.289 dB over the baseline, and SSIM values of 0.906. These results underscore the efficacy of these novel architectures in improving image quality and advancing image processing techniques, particularly in scenarios requiring heightened resolution and noise reduction.

Keywords

• Autoencoders
• data reconstruction
• noise reduction
• loss function
• architecture
• and high-resolution imaging enhancement