  We revisit the error correction scheme of real-valued signals when the
codeword is corrupted by gross errors on a fraction of entries and a small
noise on all the entries. Combining the recent developments of approximate
message passing and the spatially-coupled measurement matrix in compressed
sensing we show that the error correction and its robustness towards noise can
be enhanced considerably. We discuss the performance in the large signal limit
using previous results on state evolution, as well as for finite size signals
through numerical simulations. Even for relatively small sizes, the approach
proposed here outperforms convex-relaxation-based decoders.
