Benchmark study of deep super-resolution models for digital holography: quantitative phase and intensity evaluation

Abstract

Despite good axial resolution in holographic microscopy, lateral resolution remains constrained by optical limitations, pixel size, and noise. These restrictions hinder accurate reconstruction of fine structural and phase details. To address this, we evaluate three deep-learning super-resolution models (RCAN, SwinIR, and a conditional diffusion network) on 1,440 off-axis digital holograms of microbeads downsampled by 2 x, 3 x, and 4 x. We compare their performance to bicubic spline interpolation using PSNR, SSIM, MSE, and phase-derived depth errors. RCAN and SwinIR yield the most accurate reconstructions, preserving structural and quantitative phase information, and offering guidance on model selection for phase-focused holography.