Face photo-drawing conversion based on multi-scale feature-enhanced generative adversarial networks

This paper introduces a novel face photo-to-sketch synthesis method using a multi-scale feature-enhanced generative adversarial network (MFEGAN). The MFEGAN framework captures features at various scales through a multi-scale feature extraction module, enhanced by an attention mechanism. An improved attention residual block in the generator adaptively refines deep image features, improving overall quality. A pre-trained feature extraction network extracts and fuses face-specific features, enriching identity information. Multi-scale perceptual and focal frequency losses optimize detail quality, aligning with human perception. Experimental results show that MFEGAN outperforms existing methods in visual appeal and fidelity to original identity features.