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  Method and apparatus for compressive imaging deviceUSPTO Application #: 20060239336Title: Method and apparatus for compressive imaging device Abstract: A new digital image/video camera that directly acquires random projections of the incident light field without first collecting the pixels/voxels. In one preferred embodiment, the camera employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtain an image with only a single detection element while measuring the image/video fewer times than the number of pixels or voxels—this can significantly reduce the computation required for image/video acquisition/encoding. Since the system features a single photon detector, it can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers. (end of abstract) Agent: 24ip Law Group Usa, PLLC - Annapolis, MD, US Inventors: Richard G. Baraniuk, Dror Z. Baron, Marco F. Duarte, Ilan N. Goodman, Don H. Johnson, Kevin F. Kelly, Courtney C. Lane, Jason N. Laska, Dharmpal Takhar, Michael B. Wakin USPTO Applicaton #: 20060239336 - Class: 375216000 (USPTO) Related Patent Categories: Pulse Or Digital Communications, Apparatus Convertible To Analog The Patent Description & Claims data below is from USPTO Patent Application 20060239336. Brief Patent Description - Full Patent Description - Patent Application Claims
CROSS-REFERENCE TO RELATED APPLICATIONS [0001] The present application claims the benefit of the filing dates of U.S. Provisional Application Ser. No. 60/673,364 entitled "Method and Apparatus for OpticalImage Compression," and filed on Apr. 21, 2005; U.S. Provisional Application Ser. No. 60/679,237 entitled "Method and Apparatus for Reconstructing Data from Multiple Sources," and filed on May 10, 2005; U.S. Provisional Application Ser. No. 60/729,983 entitled "Random Filters for Compressive Sampling and Reconstruction," and filed on Oct. 25, 2005; U.S. Provisional Application Ser. No. 60/732,374 entitled "Method and Apparatus for Compressive Sensing for Analog-to-Information Conversion," and filed on Nov. 1, 2005; U.S. Provisional Application Ser. No. 60/735,616 entitled "Method and Apparatus for Distributed Compressed Sensing," and filed on Nov. 10, 2005; and U.S. Provisional Application Ser. No. 60/759,394 entitled "Sudocodes: Efficient Compressive Sampling Algorithms for Sparse Signals," and filed on Jan. 16, 2006. [0002] The above cross-referenced related applications are hereby incorporated by reference herein in their entirety. BACKGROUND OF THE INVENTION [0004] 1. Field of the Invention [0005] The invention relates to imaging devices such as cameras, video cameras, microscopes, and other visualization techniques, and more particularly, to the acquisition of images and video using fewer measurements than previous techniques. [0006] 2. Brief Description of the Related Art [0007] The large amount of raw data acquired in a conventional digital image or video often necessitates immediate compression in order to store or transmit that data. This compression typically exploits a priori knowledge, such as the fact that an N-pixel image can be well approximated as a sparse linear combination of K<<N wavelets. These appropriate wavelet coefficients can be efficiently computed from the N pixel values and then easily stored or transmitted along with their locations. Similar procedures are applied to videos containing F frames of P pixels each; we let N=FP denote the number of video "voxels". [0008] This process has two major shortcomings. First, acquiring large amounts of raw image or video data (large N) can be expensive, particularly at wavelengths where CMOS or CCD sensing technology is limited. Second, compressing raw data can be computationally demanding, particularly in the case of video. While there may appear to be no way around this procedure of "sample, process, keep the important information, and throw away the rest," a new theory known as Compressive Sensing (CS) has emerged that offers hope for directly acquiring a compressed digital representation of a signal without first sampling that signal. See Candes, E., Romberg, J., Tao, T., "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inform. Theory 52 (2006) 489-509; David Donoho, "Compressed sensing," IEEE Transactions on Information Theory, Volume 52, Issue 4, April 2006, Pages: 1289-1306; and Candes, E., Tao, T., "Near optimal signal recovery from random projections and universal encoding strategies," (2004) Preprint. [0009] Traditional methods of conserving power in camera monitoring and surveillance applications have either relied upon scheduling sleeping and awake modes, or supplementary sensors such as infrared motion detectors to decide when to power on the camera. In the former case, scheduled power-off periods could result in missing an important event entirely. In the latter case, we require additional hardware that may be costly or undesirable. Moreover, in both cases the system suffers from a "power-on lag," which delays image or video capture, potentially causing the camera to miss the important event. These problems would be solved by allowing the camera to continuously monitor the scene in a low-power, low-rate mode, and by enabling it to immediately increase its rate when an important or interesting event occurs. This kind of scheme is impossible in the traditional digital camera paradigm, which is an all-or-nothing scheme: either an image/video is captured at full rate, or no image/video is captured at all. Thus a camera that can continuously monitor at low-rate and increase to full rate with no lag-time is not found in the art, but is directly enabled by our unique camera architecture. [0010] Other efforts on compressed imaging include Pitsianis, N. P., Brady, D. J., Sun, X.: "Sensor-layer image compression based on the quantized cosine transform," SPIE Visual Information Processing XIV (2005) and Brady, D. J., Feldman, M., Pitsianis, N., Guo, J. P., Portnoy, A., Fiddy, M., "Compressive optical MONTAGE photography," SPIE Photonic Devices and Algorithms for Computing VII (2005), which employ optical elements to perform transform coding of multispectral images. The hardware designed for these purposes uses concepts that include optical projections, group testing (see Cormode, G., Muthukrishnan, S., "Towards an algorithmic theory of compressed sensing," DIMACS Tech. Report 2005-40 (2005)), and signal inference. Two notable previous DMD-driven applications involve confocal microscopy (Lane, P. M., Elliott, R. P., MacAulay, C. E., "Confocal microendoscopy with chromatic sectioning," Proc. SPIE. Volume 4959 (2003) 23-26) and micro-optoelectromechanical (MOEM) systems (DeVerse, R. A., Coifman, R. R., Coppi, A. C., Fateley, W. G., Geshwind, F., Hammaker, R. M., Valenti, S., Warner, F. J., "Application of spatial light modulators for new modalities in spectrometry and imaging," Proc. SPIE. Volume 4959 (2003)). [0011] The present invention overcomes shortcomings of the prior approaches. Preferred embodiments of the present invention take fewer measurements than prior techniques, enable significant reduction in the resources (power, computation) required for visualization and use only a small number of physical sensors. The reduction in the size of the hardware associated with preferred embodiments of the invention further may significantly reduce costs of visualization systems. The present invention can also acquire and process streaming video data (time-varying images). Finally, the present invention can adjust its data acquisition rate according to the amount of activity in the scene it is imaging. SUMMARY OF THE INVENTION [0012] The present invention uses algorithms and hardware to support a new theory of Compressive Imaging (CI). The approach is based on a new digital image/video camera that directly acquires random projections without first collecting the N pixels/voxels. (See Takhar, D., Laska, J. N., Wakin, M., Duarte, M., Baron, D., Sarvotham, S., Kelly, K. K., Baraniuk, R. G., "A new camera architecture based on optical-domain compression," Proc. IS&T/SPIE Symposium on Electronic Imaging: Computational Imaging. Volume 6065. (2006)). Due to this unique measurement approach, it has the ability to obtain an image with a single detection element while measuring the image far fewer times than the number of pixels/voxels. Note also that additional embodiments using a plurality of detection elements can also be used. [0013] The image can be reconstructed, exactly or approximately, from these random projections by using a model, in essence to find the best or most likely image (in some metric) among all possible images that could have given rise to those same measurements. While several preferred embodiments of reconstruction are described below, it should be understood that additional techniques using or incorporating the present invention can also be used. [0014] A small number of detectors, even a single detector, can be used. Thus, the camera can be adapted to image at wavelengths of electromagnetic radiation that are currently impossible with conventional CCD and CMOS imagers. This feature is particularly advantageous, because in some cases the usage of many detectors is impossible or impractical, whereas the usage of a small number of detectors, or even a single detector, may become feasible using compressive imaging. [0015] A camera in accordance with the present invention can also be used to take streaming measurements of a video signal, which can then be recovered using CS techniques designed for either 2-dimensional (2D) frame-by-frame reconstruction or joint 3D reconstruction. This allows a significant reduction in the computational complexity of the video encoding process. [0016] An imaging system in accordance with the present invention enjoys a number of desirable features: [0017] Potentially single detector or small number of detectors: By time-multiplexing each detector, we can use a less expensive and yet more sensitive photon detectors. This is particularly important when the detector is expensive, making an N-pixel array prohibitive. A single detector camera can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers. [0018] Universality: Random and pseudorandom measurement schemes are universal in the sense that they can be paired with any signal model. Therefore, the same encoding strategy can be applied in a variety of different sensing environments; knowledge of the nuances of the environment is needed only at the reconstruction mechanism (decoder). Random measurements are also future-proof: if future research in image processing yields a better signal model then the same set of random measurements can be used to reconstruct an even better quality image or video. [0019] Encryption: A pseudorandom sequence can be generated using a simple algorithm according to a random seed. Such encoding effectively implements a form of encryption: the randomized measurements will themselves resemble noise and cannot be decoded unless an observer knows the associated seed. [0020] Robustness and progressivity: Random and pseudorandom measurements are robust in the sense that the measurements have equal priority, unlike the Fourier or wavelet coefficients in current transform coders. Thus they allow a progressively better reconstruction of the data as more measurements are obtained; one or more measurements can also be lost without corrupting the entire reconstruction. [0021] Scalability: We can adaptively select how many measurements to compute in order to trade off the amount of compression of the acquired image/video versus acquisition time; in contrast, conventional cameras trade off resolution versus the number of pixel sensors. [0022] Computational asymmetry: compressive imaging (CI) places most of its computational complexity in the decoder, which will often have more substantial computational resources than the encoder/imager. The encoder is very simple; it merely computes incoherent projections and, depending on the specific embodiment, makes few or no decisions. [0023] Still other aspects, features, and advantages of the present invention are readily apparent from the following detailed description, simply by illustrating preferable embodiments and implementations. The present invention is also capable of other and different embodiments and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the present invention. Accordingly, the drawings and descriptions are to be regarded as illustrative in nature, and not as restrictive. Additional objects and advantages of the invention will be set forth in part in the description which follows and in part will be obvious from the description, or may be learned by practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS [0024] For a more complete understanding of the present invention and the advantages thereof, reference is now made to the following description and the accompanying drawings, in which: [0025] FIG. 1 is a diagram of a compressive imaging camera in accordance with a preferred embodiment of the present invention. [0026] FIG. 2 is a diagram showing the results obtained via various imaging techniques. [0027] FIG. 3 is a diagram showing frames from a sample video sequence obtained and reconstructed using various techniques Continue reading... Full patent description for Method and apparatus for compressive imaging device Brief Patent Description - Full Patent Description - Patent Application Claims Click on the above for other options relating to this Method and apparatus for compressive imaging device patent application. ###  How KEYWORD MONITOR works... a FREE service from FreshPatents1. Sign up (takes 30 seconds). 2. Fill in the keywords to be monitored. 3. Each week you receive an email with patent applications related to your keywords. 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