Unsupervised CycleGAN-Based Model for Optimizing Depth-of-Field Effects in Photographic Image

Abstract

To address issues such as blurred foreground-background separation, low detail fidelity, and hazy background blur in traditional methods for optimizing depth-of-field effects in photographic images, this study proposes an improved unsupervised cyclic generative adversarial networks model. First, the study builds upon unsupervised cyclic generative adversarial networks, enhancing the generator through deep separable convolutions and coordinate attention modules. It replaces the dual-discriminator architecture with a class activation mapping module within the discriminator, while integrating the Retinex algorithm into post-processing to address background haze effects. Algorithm performance test results indicated that in the depth-of-field information processing scene, structural similarity reached 30.4. In the edge information processing scene, structural similarity reached 28.1. All scenes maintained optimal performance. In depth-of-field optimization tests for bow-and-net images, the model's output images achieved an average information entropy of 7.22 (compared to 7.12 for original images). It effectively mitigated typical depth-of-field defects such as background overexposure and motion blur. The model achieves a balance between depth-of-field effects, efficiency, and visual quality, generating high-resolution shallow depth-of-field images. It provides a solution for unsupervised depth-of-field optimization, applicable to post-production photography and industrial image analysis. Future performance enhancements can be achieved through multi-scene dataset expansion and end-to-end optimization.