Optimization and Performance Comparison of AOD-Net and DehazeFormer Dehazing Algorithms

Image dehazing is an effective approach for enhancing the quality of images captured under foggy or hazy conditions. Although existing methods have achieved certain success in dehazing performance, many rely on deep network architectures, leading to high model complexity and computational costs. To address this issue, this study aims to compare and optimize existing algorithms to improve dehazing performance. For this purpose, we innovatively propose a multi-scale feature-coordinated composite loss mechanism, integrating perceptual loss, Mean Squared Error, and L1 regularization to optimize two dehazing methods: AOD-Net and DehazeFormer. Extensive experiments demonstrate significant performance improvements under the multi-objective loss mechanism. For AOD-Net, the PSNR increased by 22.40% (+4.17 dB), SSIM by 3.62% (+0.0318), VSNR by 43% (+1.54 dB), and LPIPS decreased by 56.30% (−0.1161). Similarly, DehazeFormer showed notable enhancements: the PSNR improved by 11.43% (+2.45 dB), SSIM by 0.8% (+0.008), VSNR by 2.6% (+0.23 dB), and LPIPS decreased by 5.5% (−0.0104). These results fully validate the effectiveness of the composite loss mechanism in enhancing the feature representation capability of the models.