Title: Exploring Spectral Characteristics for Single Image Reflection Removal Show Chinese title

Title: 探索单图像反射去除的光谱特性

Abstract: Eliminating reflections caused by incident light interacting with reflective medium remains an ill-posed problem in the image restoration area. The primary challenge arises from the overlapping of reflection and transmission components in the captured images, which complicates the task of accurately distinguishing and recovering the clean background. Existing approaches typically address reflection removal solely in the image domain, ignoring the spectral property variations of reflected light, which hinders their ability to effectively discern reflections. In this paper, we start with a new perspective on spectral learning, and propose the Spectral Codebook to reconstruct the optical spectrum of the reflection image. The reflections can be effectively distinguished by perceiving the wavelength differences between different light sources in the spectrum. To leverage the reconstructed spectrum, we design two spectral prior refinement modules to re-distribute pixels in the spatial dimension and adaptively enhance the spectral differences along the wavelength dimension. Furthermore, we present the Spectrum-Aware Transformer to jointly recover the transmitted content in spectral and pixel domains. Experimental results on three different reflection benchmarks demonstrate the superiority and generalization ability of our method compared to state-of-the-art models. Show Chinese abstract

Abstract: 消除入射光与反射介质相互作用引起的反射仍然是图像恢复领域的一个病态问题。 主要挑战来自于捕获图像中反射和透射成分的重叠，这使得准确区分和恢复干净背景的任务变得复杂。 现有方法通常仅在图像域中处理反射去除，忽略了反射光的光谱特性变化，这限制了它们有效区分反射的能力。 在本文中，我们从光谱学习的新视角出发，提出了光谱代码本以重建反射图像的光学光谱。 通过感知光谱中不同光源之间的波长差异，可以有效地区分反射。 为了利用重建的光谱，我们设计了两个光谱先验细化模块，以在空间维度上重新分配像素，并沿波长维度自适应增强光谱差异。 此外，我们提出了光谱感知变压器，以联合恢复光谱和像素域中的透射内容。 在三个不同的反射基准上的实验结果表明，我们的方法相比最先进的模型具有优越性和泛化能力。