Reinforced Residual Encoder–Decoder Network for Image Denoising via Deeper Encoding and Balanced Skip Connections
Year: 2025
Type: article
Abstract: Traditional image denoising algorithms often struggle with real-world complexities such as spatially correlated noise, varying illumination conditions, sensor-specific noise patterns, motion blur, and structural distortions. This paper presents an enhanced residual denoising network, R-REDNet, which stands for Reinforced Residual Encoder–Decoder Network. The proposed architecture incorporates deeper convolutional layers in the encoder and replaces additive skip connections with averaging operations to improve feature extraction and noise suppression. Additionally, the method leverages an iterative refinement approach, further enhancing its denoising performance. Experiments conducted on two real-world noisy image datasets demonstrate that R-REDNet outperforms current state-of-the-art approaches. Specifically, it attained a peak signal-to-noise ratio of 44.01 dB and a structural similarity index of 0.9931 on Dataset 1, and it obtained a peak signal-to-noise ratio of 46.15 dB with a structural similarity index of 0.9955 on Dataset 2. These findings confirm the efficiency of our method in delivering high-quality image restoration while preserving fine details.
Source: Big Data and Cognitive Computing
DOI: 10.3390/bdcc9040082
Authors: Ismail Boucherit, Hamza Kheddar
Institution: University Yahia Fares of Medea
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4409012322