Enhanced Dual-Pixel Image Reflection Removal via Gaussian Splatting

Image de-reflection is a critical task in computer vision. Existing methods for de-reflection using monocular cameras face challenges due to the lack of depth cues to separate the transmission and reflection layers, particularly under strong illumination or multi-layer reflection scenarios. Although recent advances, such as 3D Gaussian Splatting (3DGS), utilize novel view-synthesis capabilities to separate transmitted and reflected layers, they still encounter difficulties in practice with monocular images. In this paper, we simplify the de-reflection task by combining dual-pixel (DP) technology with 3DGS, forming the first unsupervised de-reflection framework. Specifically, we propose the Dual-View Coordinated Reflection Removal (DCRR) Framework, which integrates depth cues from DP sensors with the rendering capabilities of 3DGS. The DCRR utilizes a dual-view approach that estimates the image transmission layer and opacity via differentiable rasterization with 3DGS and reconstructs the reflection layer through a lightweight multi-layer perceptron. We then present the Dual-Pixel-Driven Reflection Gaussian Pruning (DPRGP) to refine the separation process. By using the physical properties of DP sensors, DCRR achieves significant accuracy improvements in complex reflection scenarios. A real-world DP-based dataset that includes paired reflection/reflection-free images has been collected. Extensive experiments demonstrate our competitive performance compared to state-of-the-art de-reflection approaches.