TSVD and autoencoder for image restoration: a comparative study

This study compares two image restoration approaches — TSVD and Autoencoder — applied to low-resolution natural images from the BSD100 dataset. SVD is first introduced to reveal the low-rank structure of images — a few dominant singular values carry most of the visual information while the small ones carry mostly noise — which motivates the use of its truncated variant, TSVD. Since both the original high-resolution and low-resolution versions are available, the high-resolution images serve as ground truth for quantitative evaluation. Each method is evaluated using PSNR and SSIM metrics to determine which approach most effectively restores image quality. By comparing a classical linear algebra approach and a deep learning approach under the same controlled conditions, this research aims to provide practical insight into which method works best for general image restoration tasks.

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