Image dehazing algorithms based on Transformer achieve good dehazing effects， but there are problems such as large number of network parameters and low dehazing speed. In order to prune redundant parts of the 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. Firstly， a 5-level Transformer was used to construct a pre-training model of dehazing network. Then， the 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， an evolutionary algorithm was employed to solve the optimal pruning rate sequence， so as to obtain the optimal sub-network of the pre-trained model. Experimental results show that after pruning， the number of network parameters is controlled to 0.476×106， which is reduced by 28.8% compared with that before pruning， and the dehazing time is shortened by 25.0%. On the synthetic hazy dataset RESIDE-6K， the proposed method has the Peak Signal-to-Noise Ratio （PSNR） reached 29.60 dB， and the Structural SIMilarity （SSIM） reached 0.968 7， which are reduced by only 1.63% and 0.46% compared with those before pruning， respectively. It can be seen that in terms of both quantitative and qualitative evaluation， the proposed method performs well with great reduction of the model parameters and improvement of the image dehazing speed while maintaining the quantitative indices and subjective perception basically.
