A Scalable Image Processing Framework for gigapixel Mars and other celestial body images

The Mars Reconnaissance Orbiter´s HiRISE (High Resolution Imaging Science Experiment) camera takes the largest images of the Martian surface. The image size is typically around 2.52 gigapixels. There is only a handful of software capable of doing a task as simple as reducing the size of the image by half and saving the result as a new image. The Scalable Image Processing Framework (SIPF) overcomes these issues by creating a generalized tile-based processing pipeline that loads only a small portion of the image into memory. This allows for the data in memory at any given time to become manageable. Image tiles are an intrinsic property that provides scalability and efficiency while processing images. Distributed computing technologies such as cloud computing can be applied naturally. A mathematical framework for scalable image operations is defined that provides insight into the scalable considerations needed with each class of operations. We also formalize the deferred execution design pattern and show how it is used as a basis for our implementation. The SIPF has the ability to perform a variety of scalable image operations such as cropping, rotation, scaling (bilinear and nearest neighbor interpolation), edge detection, sharpening, convolution (filters), brightness, contrast, and Gaussian blurring. The Scalable Image Processing Framework will be used to process incoming images from the Mars Exploration Rovers and eventually the Mars Science Laboratory. It will be integrated with the Maestro software (science visualization and planning tool). Maestro is used for the Mars Exploration Rover Mission and other celestial body exploratory missions.