Deep Neural Representation Guided Face Sketch Synthesis.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Image Processing

Background:

• Current face sketch synthesis methods struggle with noise and blurred edges due to RGB space manipulation.
• Existing techniques fail to fully restore facial structures after denoising.
• Neural network feature maps show promise for texture and artistic image generation.

Purpose of the Study:

• To reformulate face sketch synthesis as a neural network feature map optimization task.
• To improve the accuracy and stylistic fidelity of synthesized face sketches.
• To overcome limitations of existing methods in handling noise and preserving facial details.

Main Methods:

• The problem is reframed as a neural network feature map optimization task.
• Enhanced 3D PatchMatch and cross-layer cost aggregation are used to derive target feature maps.
• The method leverages stylistic information from training datasets.

Main Results:

• The approach accurately captures hand-drawn sketch styles.
• High-quality visual effects are achieved on multiple benchmark datasets (CUHK, AR, XM2VTS, CUFSF).
• Synthesized sketches exhibit improved detail and reduced noise compared to previous methods.

Conclusions:

• The proposed feature map-based optimization offers a superior approach to face sketch synthesis.
• This method effectively imitates hand-drawn aesthetics while preserving facial structure.
• The technique demonstrates significant potential for applications requiring realistic face sketches.