Forensic sketch-to-photo transformation with improved Generative Adversarial Network (GAN)

Forensic sketch-to-photo transformation is a critical technique in criminal investigations in which forensic artists detail their drawings based on eyewitness descriptions. However, sketch-to-photo traditional methods are not an exception to the usual differences between sketch detail and photograph style. This system aims at improving the accuracy and realism of sketch-to photo transformation through an improved Generative Adversarial Network. The system aims to leverage advanced GAN architectures to bridge the gap between sketches and photos by learning intricate mappings and stylistic nuances. The GAN model comprises two neural networks: a generator and a discriminator. The generator synthesizes photo-realistic images from forensic sketches while the discriminator evaluates the authenticity of the generated images, iteratively refining the generator's output.