On Controlling Light Transport in Poor Visibility Environments

Flood Lighting
Polarized Light Striping
Polarized light striping versus flood-lighting. In this experiment, the scene is comprised of objects immersed in murky water. Using the polarized light striping approach, we can control the light transport before image formation for capturing the same scene with better color and contrast. Click on the above images for high-resolution versions.
Poor visibility conditions due to murky water, bad weather, dust and smoke severely impede the performance of vision systems. Passive methods have been used to restore scene contrast under moderate visibility by digital postprocessing. However, these methods are ineffective when the quality of acquired images is poor to begin with. In this work, we design active lighting and sensing systems for controlling light transport before image formation, and hence obtain higher quality data. First, we present a technique of polarized light striping based on combining polarization imaging and structured light striping. We show that this technique out-performs different existing illumination and sensing methodologies. Second, we present a numerical approach for computing the optimal relative sensor-source position, which results in the best quality image. Our analysis accounts for the limits imposed by sensor noise.


"On Controlling Light Transport in Poor Visibility Environments"
Mohit Gupta, SG Narasimhan, YY Schechner,
IEEE Computer Vision and Pattern Recognition (CVPR),




Experimental Setup:
Our experimental setup consisting of a glass tank, filled with moderate to high concentrations of milk. An LCD projector illuminates the medium with polarized light. The camera (with a polarizer attached) observes a contrast chart through the medium.
Comparison of various illumination and sensing techniques:
We compare the performance of various techniques such as flood-lighting, polarized flood-lighting, light striping and polarized light-striping. We consider moderate and heavy scattering conditions. We can notice improvement in contrast using our technique of polarized light-striping over previous techniques. See full size image to avoid breaking-up text.
Limitations of the high-frequency illumination based method:
In the presence of moderate to heavy volumetric scattering, the direct component images have low SNR. The global image is approximately the same as a flood-lit image, and hence, suffers from low contrast.
Unpolarized vs. Polarized Light Stripe Scanning:
Using polarization reduces backscatter, thereby enabling reliable detection of the intersection of light sheet with the object. Thus, we can improve image contrast considerably using polarization imaging+light stripe scanning.
What is the optimal sensor-source separation for flood-lighting?:
Large separation (60 cms) results in heavy image noise. On the other hand, optimal separation (40 cms) results in a high contrast, low noise image Both the frames were captured with the same exposure time.
What is the optimal light stripe scan for the best image quality?:
Using computer simulations, we can design the optimal light stripe scan for the best image quality. The image quality is quantified in terms of the image contrast, image SNR, and the gradient across the edge of the stripe resulting from the intersection of the light sheet with the object.


This research was supported in parts by Grants # ONR N00014-08-1-0330, NSF CAREER IIS-0643628, NSF CCF-0541307 and the US-Israel Binational Science Foundation (BSF) Grant # 2006384. Yoav Schechner is a Landau Fellow - supported by the Taub Foundation. Yoav¡¯s work was conducted in the OllendorffMinerva Center. Minerva is funded through the BMBF.