Will Swin add value over other architectures?

[electricalgorithm]

Short answer: Yes — but only when used inside a physics-aware/hybrid framework.

Why:

• Swin’s windowed self-attention + hierarchical features capture both local interference detail (speckle, local twin patterns) and mid/long-range dependencies (phase correlations) which are important for separating conjugate/twin components.

• Empirically, transformer/Swin backbones have matched or outperformed CNNs in denoising / reconstruction tasks in OCT/MRI/image restoration, especially when the problem benefits from global context.

• BUT: without embedding the forward physics (forward propagation constraint, propagation layer, or physics loss) you risk hallucinations and incomplete twin suppression. PadDH and physics-ViT works explicitly show physics coupling is crucial.

So the value-add of Swin: strong feature extractor that, when combined with physics constraints, should give better generalization and better twin suppression than many CNN-only baselines.