Highlight mask-guided adaptive residual network for single image highlight detection and removal

Abstract

Specular highlights detection and removal is a challenging task. Although various methods exist for removing specular highlights, they often fail to effectively preserve the color and texture details of objects after highlight removal due to the high brightness and nonuniform distribution characteristics of highlights. Furthermore, when processing scenes with complex highlight properties, existing methods frequently encounter performance bottlenecks, which restrict their applicability. Therefore, we introduce a highlight mask-guided adaptive residual network (HMGARN). HMGARN comprises three main components: detection-net, adaptive-removal network (AR-Net), and reconstruct-net. Specifically, detection-net can accurately predict highlight mask from a single RGB image. The predicted highlight mask is then inputted into the AR-Net, which adaptively guides the model to remove specular highlights and estimate an image without specular highlights. Subsequently, reconstruct-net is used to progressively refine this result, remove any residual specular highlights, and construct the final high-quality image without specular highlights. We evaluated our method on the public dataset (SHIQ) and confirmed its superiority through comparative experimental results.