HTEB: Hybrid Transformer Enhancement Block for Robust Image Flare Suppression

Abstract

Image flare artifacts, caused by internal light reflections and scattering in camera lenses, degrade image quality by introducing unwanted bright spots, ghosting, and haze. These artifacts distort scene details and negatively impact computer vision applications. To address this, we propose a deep learning model that effectively removes flares from single images while preserving critical visual content. Our method first estimates and refines a depth map of the input image using a Dense Vision Transformer (DPT). The refined depth map is combined with the original RGB image to form a 4 -channel input, which is processed by a U-shaped network. This network uses encoderdecoder blocks to progressively suppress flares, with a novel Hybrid Transformer Enhancement Block (HTEB) at its core to model global relationships and local details. Experiments on the Flare7K++ benchmark and real-world images demonstrate that our approach outperforms existing methods in flare removal accuracy and detail preservation, proving its robustness for practical use. This work underscores the potential of hybrid attention-frequency mechanisms in transformer-based restoration architectures.