Color constancy, the human visual system's ability to perceive consistent colors under varying illumination conditions, is crucial for accurate color perception. Recently, deep learning algorithms have been introduced into this task and have achieved remarkable achievements. However, existing methods are limited by the scale of current multi-illumination datasets and model size, hindering their ability to learn discriminative features effectively and their practical value for deployment in cameras. To overcome these limitations, this paper proposes a multi-illumination color constancy approach based on self-supervised learning and knowledge distillation. This approach includes three phases: self-supervised pre-training, supervised fine-tuning, and knowledge distillation. During the pre-training phase, we train Transformer-based and U-Net based encoders by two pretext tasks: light normalization task to learn lighting color contextual representation and grayscale colorization task to acquire objects' inherent color information. For the downstream color constancy task, we fine-tune the encoders and design a lightweight decoder to obtain better illumination distributions with fewer parameters. During the knowledge distillation phase, we introduce a hybrid knowledge distillation technique to align CNN features with those of Transformer and U-Net respectively. Our proposed method outperforms state-of-the-art techniques on multi-illumination and single-illumination benchmarks. Extensive ablation studies and visualizations confirm the effectiveness of our model.

Color constancy is an important ability of the human visual system to perceive constant colors across different illumination. In this paper, we study a more practical yet challenging task, removing color cast by multiple spatial-varying illumination. Previous methods are limited by the scale of the current multi-illumination datasets, which hinders them from learning more discriminative features. Instead, we first propose a self-supervised multi-illumination color constancy model that leverages multiple pretext tasks to fully explore lighting color contextual information and inherent color information without using any manual annotations. During the pre-training phase, we train multiple Transformer-based encoders by learning multiple pretext tasks: (i) the local color distortion recovery task, which is carefully designed to learn lighting color contextual representation, and (ii) the colorization task, which is utilized to acquire inherent knowledge. In the downstream color constancy task, we fine-tune the encoders and design a lightweight decoder to obtain better illumination distributions with fewer parameters. Our lightweight architecture outperforms the state-of-the-art methods on the multi-illuminant benchmark (LSMI) and got robust performance on the single illuminant benchmark (NUS-8). Additionally, extensive ablation studies and visualization results demonstrate the effectiveness of integrating lighting color contextual and inherent color information learning in a self-supervised manner.