In today’s environment, people are becoming increasingly interested in their appearance. However, they are afraid of their unknown appearance after a plastic surgery or treatment. Accidents, burns and genetic problems such as bowing of body parts of people have a negative impact on their mental health with their appearance and this makes them feel uncomfortable and underestimated. Our approach presents a revolutionary deep learning-based image inpainting method that analyses the various picture structures and corrects damaged images. In our model, we are aiming to propose the in-painting of medical images with Stable Diffusion Model. Later on, the method that gives the best accuracy and makes the image looks real giving the best result will be used. Image inpainting is the process of reconstructing missing and damaged areas of an image is a key progress facilitated by deep neural networks. The system uses the input of the user of an image to indicate a problem, the system will then modify the image and output the fixed image, facilitating for the patient to see the final result.

The main goals for Rejuvenate:

1. Improve trust between patients and surgeons.

2. Let patients see their appearance post-surgery.

3. If there more than one stage to reach the best result, it will be determined.

The system aims to:

1. Surgeon shall select the patient’s issue.

2. System shall take the pictures of patients to let the surgeon examine it.

3. In the pre-processing stage, images will be converted to ’RGB’, then will be resized to (512,512) pixels.

4. Surgeon shall decide the wanted part from the image to start working on it.

5. Images shall be edited to meet the requirements of the patient.

6. Patient shall preview his/her appearance before the plastic surgery and giving their comments on it