• Kirti Rajadnya
• Sarwesh Nandeshwar
• Mohit Shukla
• Rahul Singh

This model uses deep learning and computational photography techniques to create synthetic refocusing of images. It lets you control the depth of field and focus on specific objects in a range of applications, such as computer vision, photography, and microscopy. The model is flexible and adaptable, thanks to its ability to estimate depth maps by analyzing spatial frequencies and depth cues. This results in highly precise depth maps that can be used to create multiple versions of an image with varying levels of blur. This allows you to have more control over the refocusing process, giving you the ability to highlight specific regions or objects within an image. Synthetic refocusing is a useful tool for both creative and practical aspects of image focus control, and this model provides valuable insights for researchers and practitioners alike.

Synthetic Refocusing, Depth Map, Computer Vision, Deep Learning, Depth of Field.

"SYNTHETIC REFOCUSING OF AN IMAGE USING MONOCULAR DEPTH ESTIMATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.132-137, June-2024, Available :http://www.jetir.org/papers/JETIRGH06021.pdf

"SYNTHETIC REFOCUSING OF AN IMAGE USING MONOCULAR DEPTH ESTIMATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp132-137, June-2024, Available at : http://www.jetir.org/papers/JETIRGH06021.pdf