Translation from sketch to realistic photo based on CycleGAN

Forensic sketches serve as crucial tools for law enforcement agencies in identifying individuals of interest. However, their effectiveness can be limited due to constraints such as incomplete information and variations in interpretation by sketch artists, often rendering these sketches unrecognizable to the general public. In response to this challenge, this paper introduces an innovative approacha CycleGAN-based image generation model. This model aims to transform monochrome forensic sketches into images with realistic colors and textures, offering an alternative visual representation that aids the public in identifying wanted individuals. The model is trained on unpaired datasets containing sketches and photographs of human faces, encompassing diverse scenarios. Through this training, it learns to generate images that closely resemble photographs captured in everyday environments. Impressively, the proposed model demonstrates rapid convergence, with both the generator and discriminator reaching optimal performance within just 500 epochs. Consequently, the generated images prove to be significantly more recognizable than the original sketches, thus enhancing the potential for successful identifications.