PhaseFuse: Recursive Fusion of Analytic Solvers for Self-Supervised Phase Retrieval

Phase retrieval reconstructs an object from intensity-only measurements, but analytic solvers can respond differently to limited and noisy observations. We study whether these complementary estimates can be combined without clean reconstruction targets or natural-image training data. PhaseFuse aligns the global phases of five analytic solver outputs and of a candidate that keeps the current estimate, then uses a compact network to predict spatial fusion weights. The normalized blend is trained on procedural phase objects using independent noisy input and target measurements. At inference, a recursive schedule restarts the solvers from the fused estimate after short update blocks, reusing the single-pass-trained selector. On 200 BSDS500 test images encoded as $128\times 128$ phase-only objects, with two coded diffraction patterns and 30 dB Gaussian measurement SNR, recursive pixel-wise fusion (PhaseFuse-PS) reaches 35.97 dB mean phase-PSNR. This is 7.27 dB above single-pass fusion and 9.32 dB above the strongest configured standalone expert, at 1.76 times the measured inference time of single-pass fusion, and every test image improves. Image-level selection (PhaseFuse-GH) reaches 36.45 dB under the same condition. Routing becomes more concentrated as the snapshot count increases. These results support further study of learned solver combination and repeated constraint enforcement under matched computation and on experimental imaging data.