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ABSTRACT
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.
Computational photography extends digital photography by providing the capability to record much more information and by offering the possibility of processing this information afterward.
INTRODUCTION
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. Many ideas in computational photography are still relatively new to digital artists and programmers. 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. The new capture methods include sophisticated sensors, electromechanical actuators and on-board processing. 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.
Computational photography extends digital photography by providing the capability to record much more information and by offering the possibility of processing this information afterward.
TRADITIONAL FILM-LIKE PHOTOGRAPHY
In traditional film-like digital photography, camera images represent a view of the scene via a 2D array of pixels. With film-like photography, the captured image is a 2D projection of the scene. Due to limited capabilities of the camera, the recorded image is a partial representation of the view.-Nevertheless, the captured image is ready for human consumption: what you see is what you almost get in the photo.
Analog and digital photography share one main limitation: They only record intensities and colors of light rays that a simple lens system projects linearly onto the image plane at a single point in time and under a fixed scene illumination. This is still mainly the principle of the camera obscura that has been known since antiquity. Thus, most of the light rays that are propagated through space and time are not recorded.
j Lens
Detector

Image
Pixel B

HISTORY & EVOLUTION OF COMPUTATIONAL PHOTOGRAPHY
The term was first used by Steve Mann, and possibly others, to describe their own research. More recently its definition has evolved to cover a number of subject areas in computer graphics, computer vision, and applied optics. These areas are given below, organized according to a taxonomy proposed by Shree Nayar.
Dennis Gabor and Gabriel Jonas Lippmann. for example, addressed part of this problem on the analog side when they invented holography and what is known as Lippmann photography. Yet, digitizing photographic recordings does allow postprocessing them digitally. Therefore, computational photography will enable features such as 3D recording, digital refocusing, synthetic re-illumination, improved motion compensation and noise reduction, and much more.
The transition from analog to digital photography has certainly been a big step that is almost complete. Although a few professionals still prefer film. Digital photography has opened many new possibilities, such as immediate image preview, postediting, or recording of short movie clips. Today's megapixel resolution of digital cameras can easily keep up with the quality of analog film for a broad range of consumer and professional applications.
This was reason enough for some of the major camera manufacturers such as Kodak, Canon, and Nikon to downscale or to cease their production of analog film cameras and film. Yet another big step lies ahead of us”and it is not too far off in the distance. It is called computational photography.

COMPUTATIONAL PHOTOGRAPHY OPTICS, SENSORS AND COMPUTATIONS
Computational imaging refers to any image formation method that involves a digital computer. Computational photography refers broadly to computational imaging techniques that enhance or extend the capabilities of digital photography. The output of these techniques is an ordinary photograph, but one that could not have been taken by a traditional camera.
There are four elements of Computational Photography.
(i) Generalized Optics
(ii) Generalized Sensor
(iii) Processing, and
(iv) Generalized Illumination
Spy tati tins
4D Ray Bender

Generalized Sensor
Ray Reconstruction


Picture

Upto 4D Ray Sampler


PIXELS VERSUS RAYS
In traditional film-like digital photography, camera images represent a view of the scene via a 2D array of pixels. Computational Photography attempts to understand and analyze a ray-based representation of the scene. The camera optics encode the scene by bending the rays, the sensor samples the rays over time, and the final 'picture' is decoded from these encoded samples. The lighting (scene illumination) follows a similar path from the source to the scene via optional spatio-temporal modulators and optics. In addition, the processing may adaptively control the parameters of the optics, sensor and illumination.
-Lighting: ray sources
-Optics: ray bending/folding devices
-Sensor: measure light
-Processing: assess it
-Display: reproduce it
Ancient Greeks says that 'eye rays' wipe the world to feel its contents...
GREEKS: Photog. seems obvious because what we gather can be described by ray geometry”if we think of our retina as a sensory organ, we 'WIPE' it across the scene, as if light let our retina 'reach out' and touch' what is around us. So let's look further into that:; lets consider light as a way of exploring our surroundings without contact, a magical way of transporting the perceivable properties of our surroundings into our brain. EVEN THE GREEKS knew this idea well”they used RAYS in exploration of vision, and described how rays going through a small aperture mapped angle to position.
ENCODING AND DECODING
The encoding and decoding process differentiates Computational Photography from traditional 'film-like digital photography'. With film-like photography, the captured image is a 2D projection of the scene. Due to limited capabilities of the camera, the recorded image is a partial representation of the view. Nevertheless, the captured image is ready for human consumption: what you see is what you almost get in the photo. In Computational Photography, the goal is to achieve a potentially richer representation of the scene during the encoding process. In some cases, Computational Photography reduces to 'Epsilon Photography', where the scene is recorded via multiple images, each captured by epsilon variation of the camera parameters. For example, successive images (or neighboring pixels) may have a different exposure, focus, aperture, view, illumination, or instant of capture. Each setting allows recording of partial information about the scene and the final image is reconstructed from these multiple observations. In other cases, Computational Photography techniques lead to? Coded Photography? where the recorded image appears distorted or random to a human observer. But the corresponding decoding recovers valuable information about the scene.
COMPUTATIONAL PHOTOGRAPHY - COMPONENTS
There are four elements of Computational Photography.
(i) Generalized Optics
(ii) Generalized Sensor
(iii) Processing, and
(iv) Generalized Illumination
Programmable Lighting

Generalized Optics : It consist of SAMP Camera and Camera Array
SAMP Camera is Single Axis Multiple Parameters camera.we can have multiple parameters in a single axis camera. Parameters vary in focus, exposure, and aperture.
Camera array is an array of same type cameras tahat are arranged in a particular order to get continuous image of an object. We can create a foreground segmentation of the picture from that we can extract the foreground image from the picture.
Generalized Sensor : It consist of gradient sensing and flutter and shutter. Gradient sensing camera Sensing Difference between Neighboring Pixels. High Dynamic Range ImagesSensing Pixel Difference with Locally Adaptive Gain.
Processing : It mainly consist of image fusion. Different view of same picture are taken by same camera by changing few parameters of the camera. And finally these images are fused together to form a new real world capture.
Generalized Illumination : It mainly consist of multiflash illumination, it is a new illumination method. It has a light source, also called programmable light
CONCLUSION
Computational photography is an extension of digital photography. It provides the capability to record much more information and offer the possibility of processing this information afterward. Computational imaging refers to any image formation method that involves a digital computer. Computational photography refers broadly to computational imaging techniques that enhance or extend the capabilities of digital photography
Computational photography transforms photography from single-instant-direction toward multiple-instant-direction imaging and illumination. It 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 are just some of the new applications found in Computational Photography.
FUTURE SCOPE
Computational photography extends digital photography. Digital photography has opened many new possibilities, such as immediate image preview, post editing, or recording of short movie clips. Today's mega pixel resolution of digital cameras can easily keep up with the quality of analog film for a broad range of consumer and professional applications.
This was reason enough for some of the major camera manufacturers such as Kodak, Canon, and Nikon to downscale or to cease their production of analog film cameras and film. Yet another big step lies ahead of us”and it is not too far off in the distance. It is called computational photography.
Most digital cameras allow capturing small movie sequences. Instead of a simple playback, however, future cameras will register the corresponding video frames into a spacetime slab. This data structure, together with appropriate processing techniques, offers higher image quality”less noise, larger depth of field, higher dynamic range”and opens completely new possibilities, such as consistent group shooting, motion-invariant image stitching, or playback of motion loops. Michael F. Cohen and Richard Szeliski describe this technique in "The Moment Camera."
New interactive forms of photos that are partly snapshots and partly videos are just some of the new applications found in Computational Photography.
New imaging devices that capture a much larger number of light rays that travel in many parameterized directions”called the 4D light field. This novel concept truly revolutionizes digital imaging in many areas and enables new applications, such as multiperspective panoramas and synthetic aperture photography.
Research breakthroughs in 2D image analysis/synthesis, coupled with the growth of digital photography as a practical and artistic medium, are creating a convergence between vision, graphics, and photography. A similar trend is occurring with digital video. At the same time, new sensing modalities and faster CPUs have given rise to new computational imaging techniques in many scientific disciplines. Finally, just as CAD/CAM and visualization were the driving markets for computer graphics research in the 1970s and 1980s, and entertainment and gaming are the driving markets today, a driving market 10 years from now will be consumer digital photography and video. In light of these trends, we can consider the computational photography is the next big step in computer graphics.
BIBLIOGRAPHY
1. Special issue on Computational Photography, IEEE Computer, August 2006.
2. Symposium on Computational Photography and Video (MIT, May 23¬25.2005)
3. http://photo.csail.mit.edu/
4. wikipedia.org
5.
CONTENTS
Pa»e No:
INTRODUCTION 4
TRADITIONAL FILM-LIKE PHOTOGRAPHY 5
HISTORY AND EVOLUTION OF COMPUTATIONAL
PHOTOGRAPHY 6
COMPUTATIONAL PHOTOGRAPHY
OPTICS, SENSORS AND COMPUTATIONS 7
PIXELS VERSUS RAYS 8
ENCODING AND DECODING 9
COMPUTATIONAL PHOTOGRAPHY
COMPONENTS 10
CONCLUSION 12
FUTURE SCOPE 13
BIBLIOGRAPHY 15