Differential Unfolding: Efficient Unfolding Reconstruction for Video Snapshot Compressive Imaging

• URL: http://arxiv.org/abs/2606.24153v1
• Date: Tue, 23 Jun 2026 05:20:50 GMT
• Title: Differential Unfolding: Efficient Unfolding Reconstruction for Video Snapshot Compressive Imaging
• Abstract summary: Deep Unfolding Networks (DUNs) dominate video Snapshot Compressive Imaging (SCI)<n>Existing methods repeatedly stack high-complexity priors with identical structures, ignoring the fact that optimization trajectories converge toward static states.<n>We present Differential Unfolding (DU), a heterogeneous framework that replaces uniform repetition with dynamic evolution.
• Score: 15.893464403713082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Deep Unfolding Networks (DUNs) dominate video Snapshot Compressive Imaging (SCI), they remain constrained by a uniform design philosophy. Existing methods repeatedly stack high-complexity priors with identical structures, ignoring the fact that optimization trajectories converge toward static states. This results in representation stagnation, where high-cost computations are wasted on minimal feature updates. To address this inefficiency, we present Differential Unfolding (DU), a heterogeneous framework that replaces uniform repetition with dynamic evolution. Central to DU is the Differential Evolutionary Framework (DEF), which partitions the unfolding process into two complementary roles: structural anchoring and differential evolution. In this scheme, high-parameter general stages are sparsely deployed to generate high-fidelity feature foundations. Complementing these, lightweight differential stages employ a Differential Representation Prior (DRP) to propagate and refine these foundational features through a differential mechanism. By integrating Differential Representation Attention (DRA) for evolving attention maps and a Differential Modulated FFN (DM-FFN) for feature rectification, DRP effectively models cross-stage variations with minimal overhead. By focusing computational resources on dynamic evolution rather than static redundancy, DU achieves a superior trade-off between accuracy and efficiency. Extensive experiments verify that our method establishes new state-of-the-art results while significantly slashing computational overhead. https://github.com/Muyuan-Zhang/DU

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