LeeKosarajuSankaranarayanan
Introduction
The human visual system responds to only wavelengths of light in the range 400-700nm. Although not perceived by the human eye, light in the wavelengths from about 800-2500nm, which represents the near-infrared region of the electromagnetic spectrum, is rich in information about the scene that can be utilized for a variety of applications. Remote sensing community has a long history of doing this. Since silicon is sensitive to these wavelengths, near-infrared capture could be enabled in digital cameras and the information could be used to enhance photographic images. A number of such applications have been proposed by researchers [place holder for references]. In our work, we are concerned with evaluating some of these algorithms used for contrast enhancement, haze removal and skin smoothing.
As these applications work independent of each other, methods and results are discussed separately for each of these.
Contrast Enhancement
Methods
Results
Haze Removal
Methods
Results
Skin Smoothing
Motivation
As is the motivation of this project in general, the skin smoothing portion is designed to be an automation of a commonly performed post processing technique. In many cases, photographs (specifically portraits) are edited to remove small unwanted characteristics in faces. This can be seen in images of models in ads, actors in movie posters, and professional grade portraits. It is common to "touch up" a photograph by removing small wrinkles, blemishes, and freckles from the skin; often this is done in photoshop by a professional editor or with relatively complex algorithms to attempt to automate the process. It is proposed by Susstrunk and Fredembach that near infrared data can be utilized to more accurately automate this process.
The theory for using infrared is described best by the reflective properties of melanin and hemoglobin -- longer wavelength light reflects less. This means that near infrared light will penetrate further into the skin than that in the visible spectrum. With that in mind, it can be shown that infrared images effectively capture the deep, important structural information of a portrait, while not capturing the shallow, unwanted information. For example, freckles and small wrinkles do not show up in an infrared image, but the edges that define the eyes, mouth, and nose do. Using this as a basis for the theorem, this portion of the project explored possible algorithms to generate smoothed images that maintain all of the structural information intended to be kept in the final portrait.
Methods
Results
Conclusion and Future Directions
Acknowledgements
We would like to thank Dr.Joyce Farrell and Dr.Torbjorn Skauli for providing us with copious hyperspectral data and also for their help throughout the project. We also appreciate the feedback from Prof.Brian Wandell and Henryk Blasinski that helped us refine our project thought process. I'm missing some people here, Dr.Hagit, yes? please add people who helped you Serene and Evan