A flare removal network for night vision perception: Resistant to the interference of complex light

Abstract

Abstract The high‐precision visual perception results are easily affected by the lens flare issue when the image sensor is facing to strong light. The existing flare removal methods have poor robustness when confronted with flare interference caused by complex nighttime lighting, which has to preserve natural light source information. A simulated dataset for the removal of night flares is created to solve the problem of collecting complete paired training data, and night flare removal network (NFR‐Net) is proposed to remove the interference caused by various light disturbances at night. The light source extraction module is introduced to retain light source information realistically and effectively in night vision scenes. Extensive experimental results demonstrate that the proposed method is superior to the existing related methods in the various complex night vision scenes. The proposed NFR‐Net can enhance visual perception of nighttime images significantly and improve the performance of night vision tasks.

Keywords

• computer vision
• convolutional neural nets
• image enhancement