IllumiNet: A two-stage model for effective flare removal and light enhancement under complex lighting conditions

This is the authors' abstract. We don't add key points for this paper.

Images captured under challenging lighting conditions suffer from both uneven illumination and flare artifacts (e.g., glare, streaks, and shimmer). Existing image enhancement methods mainly focus on either enhancing low-light regions or removing flares, but rarely address both issues simultaneously. When these methods are applied in sequence, they inevitably lead to over-enhancement and saturation in bright regions affected by flares or insufficient enhancement in low-light areas. In this article, we introduce a neural network model – IllumiNet, to enhance images captured under complex lighting conditions. Specifically, we propose a two-stage pixel-to-pixel generative model , that achieves both image flare removal and image light enhancement. Each stage is structured based on the U-Net model with the Mix Vision Transformer as the backbone, which is shared by the two stages to support domain knowledge interaction between the two tasks while helping to reduce model size. Additionally, we introduce a training strategy that combines knowledge distillation with simulated data generation, eliminating the need for real-paired datasets. Experiments across various benchmarks demonstrate that IllumiNet produces visually appealing enhanced images and exhibits robust generalization to diverse real-world scenarios. • We proposed a model to achieve both image flare removal and image light enhancement. • A training pipeline is developed to eliminate the need for paired image acquisition. • Comprehensive experimental results are provided for complex real-world scenarios.