Frequency-Domain Enhancement and Multi-Scale Residual Modeling for Single-Image Reflection Separation

Abstract

Single-image reflection separation aims to recover transmission and reflection layers from an image captured through glass and is an important problem in computational photography and low-level vision. Recent dual-stream interactive methods have achieved substantial progress, but most of them still rely mainly on linear spatial-domain feature modeling and single-scale residual prediction, which limits their ability to handle complex reflection mixtures. To address these issues, this paper proposes a structural enhancement method for a pretrained dual-stream baseline. Inspired by homomorphic filtering, a Homomorphic Frequency Enhancement Module (HFEM) extracts complementary frequency-domain features through a logarithmic transformation and Haar wavelet decomposition and injects them into the local-prior branches of both streams via zero-initialized projections. A multi-scale residual modeling module (MSRM) further aggregates multi-level decoder features to replace the original single-scale residual module. During training, the pretrained baseline is kept frozen, and only the newly introduced modules are optimized. The final implementation introduces 1.528 M additional trainable parameters, including 1.318 M from HFEM and 0.210 M from MSRM, accounting for approximately 0.47% of the complete 328.49 M-parameter adapted model. On five public benchmarks with 494 images in total, the proposed method improves the weighted-average PSNR/SSIM from 26.52 dB/0.916 to 26.74 dB/0.917. Although the overall gain is modest, the frozen training setting shows a more stable optimization process, with a PSNR fluctuation of 0.28 dB compared with 3.65 dB for joint fine-tuning. Ablation results indicate that HFEM is the primary source of the performance gain, while MSRM provides a complementary residual-modeling benefit.