PromptNorm: Image Geometry Guides Ambient Light Normalization

David Serrano-Lozano, Francisco A. Molina-Bakhos, Danna Xue, Yixiong Yang, Maria Pilligua, Ramon Baldrich, María Vanrell, Javier Vazquez-Corral

Abstract: Ambient lighting normalization is an important computer vision task that aims to remove shadows and standardize illumination across an entire image. While previous approaches have primarily focused on image restoration and frequency-based cues, this paper hypothesizes that incorporating image geometry can significantly improve the normalization process. We propose PromptNorm, a novel transformer-based model that leverages state-of-the-art monocular depth estimators to overcome the challenges posed by strong shadows and extreme color distortions. Our approach uniquely utilizes image normals as a guiding mechanism. We encode these normals to generate a low-level geometric representation, which is then used as Query inputs to dynamically weight the attention maps within transformer blocks. Comprehensive experimental evaluations demonstrate that PromptNorm not only outperforms existing state-of-the-art methods in ambient lighting normalization but also validates the effectiveness of integrating geometric information into image processing techniques. Both quantitative metrics and qualitative assessments confirm the effectiveness of our method.

External IDs:dblp:conf/cvpr/Serrano-LozanoM25