A Method of Single-Image Light Source Interference Removal Based on the Multitask Network

In the presence of light sources at a scene，captured images may be easily interfered with by the existence of various kinds of flares，glares，artifacts，etc. This leads to a lowered quality of key information，which adversely affects vision-based tasks such as object detection，depth estimation，and semantic segmentation based on those images. However，removing the light source interference of various forms and distributions with current methods is difficult. Therefore，we propose a light source interference removal network and its training method to solve issues regarding poor processing results，lack of paired training data，and weak generalization of existing algorithms. This network combines a multitasking structure to fully utilize the feature information of different tasks and thereby improves its interference removal performance. In addition，a postprocessing method for light source blend is presented to reduce the interference introduced in the postprocessing step. The proposed method realizes an average peak signal-to-noise ratio of 25.81 dB and 23.25 dB and a structural similarity of 0.872 6 and 0.922 3 when using public and self-built datasets，respectively. In terms of subjective qualitative comparison，the proposed method has better interference removal performance compared to existing methods. © 2024 Tianjin University. All rights reserved.