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Center Innovation Fund: SSC CIF

Optimizing Low Light Level Imaging Techniques and Sensor Design Parameters using CCD Digital Cameras for Potential NASA Earth Science Research aboard a Small Satellite or ISS

Completed Technology Project

Project Introduction

For this project, the potential of using state-of-the-art aerial digital framing cameras that have time delayed integration (TDI) to acquire useful low light level imagery was enhanced.  Computational photography is an emerging field of study pertaining to capturing, processing and manipulating digital imagery with the purpose of enhancing and improving the imagery beyond what is typically accomplished using traditional image processing techniques.

While computational photography techniques have been extensively applied to computer vision and computer graphics problems and are becoming more common in consumer cameras and mobile devices, they have only limitedly been applied within the remote sensing community. With increased computer processing power and awareness of the utility of computational photography, these techniques are now beginning to be applied to the remote sensing image processing chain. This project made use of two computational photography techniques, high dynamic range (HDR) imagery formulation and bilateral filters to enable novel imaging applications. By carefully combining multiple data sets, the effective dynamic range within the image can be increased without over or underexposing portions of the scene. Using this technique, HDR image products were produced from imagery acquired under extreme low light level conditions.

This project made use of two computational photography techniques, high dynamic range (HDR) imagery formulation and bilateral filters to enable novel imaging applications in support of developing a low light level imaging capability to improve imagery. HDR imaging is a technique that generates an image with a greater dynamic range than ordinarily achievable given an imaging system’s hardware architecture.  HDR images are generated by acquiring multiple images of the same scene at different exposure settings. Each individual image contains a collection of properly exposed pixels and pixels that are both dark (underexposed) and saturated (overexposed). HDR image products are generated by combining multiple frames of data at different exposure times such that the darkest areas within a frame are imaged with the longest exposure time and the brightest areas within a frame are imaged with the shortest exposure time.  This technique can be very powerful when processing imagery acquired under low light level conditions.  Standard imagery acquired under low light often contains a significant number of pixels that are extremely dark whereby information content is lost in the shadows. Bilateral filters reduce the noise in relatively uniform areas within an image while minimizing blurring of edges and other spatial features. Edge preserving noise reduction filters are important for improving the imagery of poorly lit scenes. These filters can be used to improve the quality of imagery acquired under low light level conditions. This type of filter preserves edges by only allowing pixels with similar radiometric values to be included in the spatial filter. By looping through each pixel within an image and assigning weights to adjacent pixels, the entire image is processed.

However, implementation of the bilateral filter can be computationally intensive, so alternative algorithms that rely on approximations were developed. The bilateral filter described above was implemented in Matlab® and a simple simulated edge target image was constructed to functionally test the algorithms. For both sets of images noise levels (2% and 4%), and the bilateral filter results were more pronounced as light level and image quality decreased. By using these two computational photography techniques, representative HDR image products with imagery acquired under extreme low light conditions were successfully produced.

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This is a historic project that was completed before the creation of TechPort on October 1, 2012. Available data has been included. This record may contain less data than currently active projects.

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