Work

Noise-Correlated Neural Network Channel Selection for Signal-to-Noise Ratio Enhancement in Holographic Data Storage

Year: 2026

Type: article

Abstract: Neural networks significantly outperform traditional methods in both decoding amplitude-, phase-, and polarization-encoded data pages and suppressing noise within them. However, the mechanism behind neural networks’ denoising capability remains not fully understood. We discover that zeroing channels can improve the reconstruction effect of the model. Consequently, this paper presents a method to locate the noise feature objectively from γ, the weights of the Batch Normalization (BN) layer. γ stands for the importance of the channel in the model and γ < 1 means the channel may contain noise feature. Through experiments, removing the channels that contained a higher proportion of these noisy features, the reconstructed data pages showed a ~2% improvement in Peak Signal-to-Noise Ratio (PSNR) compared to results obtained by directly outputting data without removing the noisy channels. It indicates that neural networks achieve efficient denoising of encoded data pages by adjusting the weight parameters of BN layers, thereby suppressing or enhancing specific channels.

Source: Photonics

DOI: 10.3390/photonics13020126

Authors: Junqian Deng, Dakui Lin, Xiao Lin, Xiaodi Tan

Institution: Fujian Normal University

Language: English

Topic: Photorefractive and Nonlinear Optics

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China