BiDeMem: Bidirectional Degradation Memory for Explainable Image Restoration

Degradation-aware prompts, conditions, and latent priors are increasingly used in image restoration, yet they are usually judged by a single endpoint: whether the restored image obtains higher PSNR.

This is a weak test of semantics.

A condition can help by adding capacity, acting as a global correction bias, or exploiting dataset shortcuts, without becoming an interpretable degradation prior.

We propose BiDeMem, a bidirectional degradation memory for explainable image restoration.

A query built from restoration features and input statistics retrieves a compact top-k subset of memory slots.

The same selected slot identity supports the restoration path at inference time and a training-only forward-degradation explanation path.

The study centers on verifiability in a controlled multi-degradation NAFNet setting.

New controls separate the gain from a correction head alone, a dense query prior, and a static global prior: these variants are 0.2588, 0.2586, and 0.2839 dB below BiRank, respectively.

Strong residual supervision and a wider degradation head also remain below the full bidirectional memory model.

Intervention probes show that BiRank preserves restoration quality while increasing wrong-prior and native-prior sensitivity, framing degradation memory as both a restoration module and a falsifiable explanation mechanism.