Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Facial images are often used in applications that need to recognize or identify persons. Many existing facial recognition tools have limitations with respect to facial image quality attributes such as resolution, face position, and artifacts present in the image.   describe a new low-cost framework for preprocessing low-quality facial images in order to render them suitable for automatic recognition. For this,  first detect artifacts based on the statistical difference beten the target image and a set of pre-processed images in the database. Next,  eliminate artifacts by an inpainting method which combines information from the target image and similar images in our database. Our method has low computational cost and is simple to implement, which makes it attractive for usage in low-budget environments.  illustrate our method on several images taken from public surveillance databases, and compare our results with existing inpainting techniques.
['Andre Sobiecki', 'Alexandru Telea', 'Gilson A. Giraldi', 'Luiz Antonio Pereira Neves', 'Carlos Eduardo Thomaz']
I am writing a research paper on a idea Low-cost Automatic Inpainting for Artifact Suppression in Facial Images
[{"from": "human", "value": "I am writing a research paper on a idea Low-cost Automatic Inpainting for Artifact Suppression in Facial Images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Facial images are often used in applications that need to recognize or identify persons. Many existing facial recognition tools have limitations with respect to facial image quality attributes such as resolution, face position, and artifacts present in the image.   describe a new low-cost framework for preprocessing low-quality facial images in order to render them suitable for automatic recognition. For this,  first detect artifacts based on the statistical difference beten the target image and a set of pre-processed images in the database. Next,  eliminate artifacts by an inpainting method which combines information from the target image and similar images in our database. Our method has low computational cost and is simple to implement, which makes it attractive for usage in low-budget environments.  illustrate our method on several images taken from public surveillance databases, and compare our results with existing inpainting techniques."}]