Introduction of Computational Photography:

Computational Photography is an interdisciplinary field that merges computer science, optics, and photography to develop innovative techniques and algorithms for enhancing, manipulating, and understanding images. It goes beyond traditional photography by leveraging computational methods to capture, process, and create images with unique and artistic effects. This research area has transformed how we perceive and interact with visual media, leading to groundbreaking advancements in photography.

Subtopics in Computational Photography:

1. Image Enhancement and Restoration: Computational Photography research focuses on developing algorithms to enhance image quality, remove noise, and restore damaged or old photographs, preserving visual memories and improving image clarity.
2. HDR Imaging (High Dynamic Range): Techniques for capturing and combining multiple exposures of an image to create stunning, high-quality photos that preserve details in both dark and bright areas, ideal for scenes with extreme lighting conditions.
3. Depth-of-Field Manipulation: Computational Photography enables the adjustment of an image's depth of field after capture, allowing for creative blurring and focusing effects to highlight specific objects or areas within a photo.
4. Panorama Stitching: Research in this subtopic involves automatically stitching multiple images together to create panoramic views, providing a broader and more immersive perspective of a scene.
5. Light Field Photography: Light field cameras capture not only the intensity but also the direction of light rays, allowing for post-capture refocusing, perspective shifting, and 3D scene reconstruction.

Computational Photography continues to push the boundaries of what is possible in image capture and manipulation, offering creative and practical solutions for photographers and visual artists. These subtopics represent some of the key areas where research and innovation are making a significant impact.