关键词: 图像去雾, Transformer, 模型剪枝, 费雪信息, 黑塞矩阵, 进化算法

Abstract: Image dehazing algorithms based on Transformer achieved good effect, but there were still problems such as large network parameters and slow dehazing speed. In order to prune the redundant parts of dehazing network directionally and shorten dehazing time without affecting dehazing quality, a Transformer image dehazing method based on component collaborative optimization pruning, CCOP-IDT (Component Collaborative Optimization Pruning Image Dehazing Transformer), was proposed. First, a 5-level Transformer was used to construct a pre-training model of dehazing network. Then, network pruning was modeled as an optimization problem. Fisher information was used to evaluate the importance of weight parameters, and Hessian matrix was used to measure the joint influence of pruning components on network output, so as to establish a collaborative optimization method for multiple pruning components. Finally, evolutionary algorithm was employed to solve the optimal pruning rate sequence, and the optimal sub-network of the pre-trained model was obtained. After pruning, the parameters of the network are controlled to 0.476MB, which reduces 28.8% compared with that before pruning, and the dehazing time is shortened by 25.0%. In the synthetic hazy dataset RESIDE-6K, the peak signal-to-noise ratio (PSNR) of the proposed method reaches 29.60dB, and the structural similarity (SSIM) reaches 0.9687, which reduces 1.63% and 0.46% compared with that before pruning, respectively. The quantitative and qualitative experimental results show that the proposed method can greatly reduce the model parameters and improve the image dehazing speed while basically maintaining the quantitative index and subjective perception.

Key words: image dehazing, Transformer, model pruning, Fisher information, Hessian matrix, evolutionary algorithm