Hybrid++: The Bridge between PDE Models and Deep Learning for Gamma Noise Removal

Key points

• This paper presents Hybrid++, a novel trainable nonlinear reaction-diffusion architecture that addresses both the concerns - staying interpretable while offering performance close to huge black-box models.1
• It combines a fully learnable PDE initialization with a 3-stage reaction-diffusion network having 64-channel multiscale filter banks, 4-layer Squeeze-and-Excitation attention-based influence functions and a 64-dimensional noise level embedding.1
• On the FoE benchmark, Hybrid++ substantially improves over classical PDE, BM3D and the original TNRD baselines.1
• We therefore position Hybrid++ not as a universal state-of-the-art image restoration backbone, but as a compact, physically structured reaction-diffusion model for multiplicative gamma noise.1

Sources (1)

• [1]Hybrid++: The Bridge between PDE Models and Deep Learning for Gamma Noise Removal
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 04:21 PM

  Multiplicative gamma noise is one of the dominant noise factors in Synthetic Aperture Radar (SAR) and medical ultrasound images.

  This paper presents Hybrid++, a novel trainable nonlinear reaction-diffusion architecture that addresses both the concerns - staying interpretable while offering performance close to huge black-box models.

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