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Computational photography is an emerging field that aims to overcome the limitations of conventional digital cameras to produce more vivid, compelling, and meaningful visualizations of the world around us. In this course, we will study ways of manipulating and combining photographs and videos to produce new and better pictures, 3D models, walkthroughs, panoramas, animations, etc. Although we will primarily focus on computational techniques that work with standard digital photographs or videos, we will also touch on the subject of designing novel and unconventional imaging devices, such as light field cameras.

Computational photography combines plentiful computing, digital sensors, modern optics, actuators, probes and smart lights to escape the limitations of traditional film cameras and enables novel imaging applications. Unbounded dynamic range, variable focus, resolution, and depth of field, hints about shape, reflectance, and lighting, and new
interactive forms of photos that are partly snapshots and partly videos are just some of the new applications found in Computational Photography. The computational techniques encompass methods from modification of imaging parameters during capture to sophisticated reconstructions from indirect measurements. We provide a practical guide
to topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples.

Many ideas in computational photography are still relatively new to digital artists and programmers and there is no upto-date reference text. A larger problem is that a multi-disciplinary field that combines ideas from computational methods and modern digital photography involves a steep learning curve. For example, photographers are not always familiar with advanced algorithms now emerging to capture high dynamic range images, but image processing researchers face difficulty in understanding the capture and noise issues in digital cameras. These topics, however, can be easily learned without extensive background. The goal of this STAR is to present both aspects in a compact form.

The new capture methods include sophisticated sensors, electromechanical actuators and on-board processing. Examples include adaptation to sensed scene depth and illumination, taking multiple pictures by varying camera parameters or actively modifying the flash illumination parameters. A class of modern reconstruction methods is also
emerging. The methods can achieve a ?photomontage? by optimally fusing information from multiple images, improve signal to noise ratio and extract scene features such as depth edges. The STAR briefly reviews fundamental topics in digital imaging and then provides a practical guide to underlying techniques beyond image processing such as gradient domain operations, graph cuts, bilateral filters and optimizations.

The participants learn about topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples. We hope to provide enough fundamentals to satisfy the technical specialist without intimidating the curious graphics researcher interested in recent advances in photography.

The intended audience is photographers, digital artists, image processing programmers and vision researchers using or building applications for digital cameras or images. They will learn about camera fundamentals and powerful computational tools, along with many real world examples.

 Download your Reports for Computational Photography


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