Reflection removal using recurrent polarization-to-polarization network - Open Access - 01.07.2026 - Research Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden. Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by (Link öffnet in neuem Fenster) Abstract 1 Introduction Reflections caused by semi-reflectors such as glass are commonly seen in daily life. When light passes through semi-reflectors, a camera inevitably captures the reflection and the transmission components at the same time. Nevertheless, most computer vision applications, such as object detection, segmentation, and depth estimation assume that each pixel value is derived only from the scene corresponding to the transmission. Therefore, reflection removal is a crucial task to improve the robustness of real-world applications using a camera. Most existing reflection removal methods are based on a single grayscale or color image, where both the input and the outputs (reflection and transmission) are in the intensity domain, as illustrated by the intensity-to-intensity model of Fig. 1a. While recent deep-learning-based methods have shown great progress [1‐7], the separation of the reflection and the transmission is still challenging due to an ill-posed problem, as an infinite number of transmission and reflection image combinations is possible to reproduce the same mixed image. Other approaches attempt to solve this problem by using multi-view color images [8‐12]. However, these methods typically necessitate image alignment as a pre-processing step, which imposes constraints on their practical application. Anzeige Meanwhile, as the price of one-shot polarization cameras has decreased, one-shot acquisition of polarized images has become much easier in recent years [13, 14]. Considering that the existence of reflection components changes the polarization state of a scene, some non-learning-based [15‐18] or learning-based [19‐21] methods solve the reflection removal by using