JP6475269B2 - Compressed sensing imaging - Google Patents

Description

  This application is a US patent application Ser. No. 13 / 658,904 entitled “Resolution and Focus Enhancement” filed Oct. 24, 2012, which is incorporated herein by reference in its entirety: United States Patent Application No. 13 / 658,900 entitled “Lensless Comprehensive Image Acquisition” filed on Oct. 24, 2012; Patent Application No. 13 / 367,413, and “Apparatus a” filed on Sep. 30, 2010 and issued on Feb. 4, 2014 as U.S. Pat. No. 8,644,376. Referring to the d Method for Generating Compressive Measurements of Video Using the subject matter of the Spatial and Temporal Integration entitled "US patent application Ser. No. 12 / 894,855.

  The present disclosure is directed to systems and methods for compressive sense image processing.

  This section introduces insights that can help to better understand the systems and methods of the present disclosure. Accordingly, the language in this section should be read in light of this and should not be understood or interpreted with respect to what is included in the prior art or what is not included in the prior art.

  Digital image / video cameras acquire and process large amounts of raw data. In order to store or transmit image data efficiently, raw pixel data is first captured for each of the N pixels of the N pixel image, and then usually using an appropriate compression algorithm, Compressed for storage and / or transmission. To reduce the size of an image (or video) captured by a camera, it is generally helpful to compress the raw data after capturing each of the N pixels of the image, but it takes a lot of computational resources and time is necessary. Furthermore, compressing the raw pixel data does not necessarily significantly reduce the size of the captured image.

  A newer technique, known as compressive sensing imaging, uses random projection to collect compressed image (or video) data without first collecting raw data for all N pixels of the N pixel image. get. For example, a series of compression measurements representing an encoded (ie, compressed) image is obtained by applying a compression measurement base. In this approach, a smaller number of compression measurements are obtained compared to the raw data for each of the N pixel values of the desired N pixel image, eliminating the need for compression after the raw data is captured. It can be greatly reduced.

Systems and methods for compressed sensing imaging are provided. In some embodiments, incident light reflected from an object and passing through an aperture array is detected by a sensor. Using the sequence matrix for compression is determined based on the attributes of the opening sequence and the sensor, based on the output from the sensor, intermediate compression measurements are generated. The intermediate compression measurement is further processed to generate a compression measurement that represents a compressed image of the object. Different from the sequence matrix that was used to obtain an intermediate compression measurements using the determined reconstruction matrix, the restored image of the object from the compression measurement is generated.

In one aspect, the system and method of compressed sensing imaging comprises generating a plurality of sequences matrix, determining a plurality of intermediate compression measurements using the multiple sequence matrix, the plurality of intermediate Generating a plurality of compressed measurements representing a compressed image of the object using the compressed measurements.

  In some aspects, the systems and methods include generating a reconstructed image of an object from a plurality of compressed measurements using a reconstructed basis matrix.

  In some aspects, the system and method determine a kernel matrix based on an aperture array of aperture elements and sensor attributes and generate the sensing matrix using the kernel matrix and the reconstructed basis matrix Including.

In some embodiments, the system and method includes generating a plurality of sequences matrix by decomposing sensing matrix.

  In some aspects, the system and method includes determining a sensitivity function of the sensor, determining at least one characteristic function for at least one of the aperture elements of the aperture array, the sensitivity function and at least one Calculating a kernel function by performing a convolution operation using two characteristic functions and determining a kernel matrix using the kernel function and an image.

  In some aspects, the system and method includes applying a sparse operator to generate a reconstructed image of the object from the plurality of compressed measurements using the reconstructed basis matrix.

In some embodiments, the system and method, during a period of time, and selectively enable or disable the one or more openings elements of the opening sequence based on at least one base in the sequence matrix, Determining at least one of the plurality of intermediate compression measurements, wherein the at least one of the plurality of intermediate compression measurements is determined based on a total amount of light detected by the sensor during the period.

  In some embodiments, the aperture array is an array of micromirrors. In some aspects, the aperture array is an array of LCD elements.

FIG. 6 illustrates an example of a compressed sensing imaging system according to various aspects of the present disclosure. According to one embodiment of the present disclosure is a diagram showing an example of a camera unit for acquiring the compressed measurements of an object using a sequence matrix. FIG. 6 illustrates an example process for compressed sensing imaging in accordance with various aspects of the present disclosure. FIG. 6 illustrates an example device for implementing aspects of the disclosure.

  Various aspects of the disclosure are described below with reference to the accompanying drawings. Like numbers in the drawings refer to like elements in the description of the drawings. The description and drawings merely illustrate the principles of the disclosure. Various structures, systems and devices are illustrated and described in the drawings and drawings for purposes of explanation only and so as to not obscure the present invention with details that are well known to those skilled in the art. Although not explicitly described or illustrated herein, the principles of the present disclosure may be embodied and various configurations may be devised that fall within the spirit and scope thereof.

  As used herein, the term “or”, unless stated otherwise (eg, or otherwise, or “or in the alternative”) Represents exclusive "or". Further, terms used to describe relationships between elements should be interpreted broadly, including direct relationships or the presence of elements intervening between elements, unless otherwise specified. For example, when an element is described as “connected” or “coupled” to another element, the element may be directly connected to or coupled to the other element An element may be present. In contrast, when an element is described as “directly connected” or “directly coupled” to another element, there are no intervening elements present. Similarly, terms such as “between”, “adjacent” should be interpreted similarly.

  The singular forms also include the plural unless the context clearly indicates otherwise. Also, the terms “comprises”, “comprising”, “includes” and “including”, as used herein, describe the feature, integer, Expressly present the presence of a step, action, element, and / or component, but does not exclude the presence of one or more other features, integers, steps, actions, elements, components, and / or groups thereof It is further understood.

FIG. 1 shows a schematic example of a compressed imaging acquisition and reconstruction system 100 (system 100). Incident light 105 reflected from the object 110 is received by the camera unit 115, camera unit 115, using the sequence matrix 1 20 of the determined number for compression to generate a plurality of intermediate compression measurements. Further, the intermediate compression measurement is processed to generate a compression measurement 125 that represents a compressed image of the object 110. A compression measurement 125 representing a compressed image of the object 110 may be stored (or transmitted) by the storage / transmission unit 130. A reconstruction unit 135 generates a reconstructed image 140 of the object 110 from the compressed measurements 125 using the determined reconstruction matrix 150 (eg, for display on a display unit).

  In FIG. 1, the units are shown separately, but this is merely for ease of understanding the present disclosure. In other aspects, any or all of the functions of the aforementioned units may be implemented using fewer or more units. Further, functions belonging to various units may be implemented by a single processing device or distributed among multiple processing devices. Some examples of suitable processing devices are cameras, camera systems, mobile phones, personal computer systems, tablets, set-top boxes, smartphones, or any type of data configured to acquire, process, or output data Including computing devices.

  In one embodiment, a single processing device may be configured to provide the functionality of each unit of system 100. A single processing device may include, for example, a memory that stores one or more instructions, and a processor for executing the one or more instructions, when the instructions are executed, the unit The processor may be configured to provide the functionality attributed to A single processing device may include other components normally provided in a computing device, such as one or more input / output components for inputting or outputting information to or from the processing device. Includes cameras, displays, keyboards, mice, network adapters and the like.

  In another embodiment, a local processing device may be provided at a first location that is communicatively interconnected with a remote processing device at a remote location via a network. The local processing device may be configured with the capability to generate a compression measurement 125 of the local object 110 and provide it to the remote processing device via the network. The remote processing device then receives the compressed measurements from the local processing device and generates a reconstructed image 140 from the compressed measurements 125 using the reconstructed basis matrix 150 in a manner described below, to the remote user. The reconstructed image may be displayed. Each of the local processing device and the remote processing device may be implemented using an apparatus similar to a single processing device, and as with a single processing device, memory that stores one or more instructions , A processor for executing the one or more instructions, and various input / output components. The network may be an intranet, the Internet, or any type or combination of one or more wired or wireless networks.

  FIG. 2 shows an example of a lensless camera unit 115 for obtaining a compressed measurement 125 representing a compressed image of the object 110 using compressed sensing imaging. Although specific embodiments of the lensless camera unit 115 are described, this should not be construed as a limitation, and the principles of the present disclosure may be applied to other embodiments of the compressed sensing imaging system.

Incident light 105 reflected from the object 110 is received by the camera unit 115, where the light 105 is selectively allowed to pass through the aperture array 220 of N individual aperture elements and strikes the sensor 230. The camera unit 115 processes the output from the sensor 230 by using a plurality of sequences matrix determined based on one or more attributes of the aperture array 220 and sensor 230, it generates an intermediate compression measurements. The compression measurement 125 collectively represents a compressed image of the object 110 and is determined using the intermediate compression measurement.

  To achieve compression, the number M of compression measurements 125 acquired as a compressed image of the object 110 is N pieces acquired with a conventional camera system having N pixel sensors to generate an N pixel image. Usually significantly less than the raw data value of. Thus, the need for conventional compression after obtaining raw data values is reduced or eliminated. In practice, the number M of compressed measurements is determined based on the desired balance between the quality of the N-pixel image 140 reconstructed using M compressed measurements and the degree of compression. It may be preselected in relation to the N aperture elements.

  The example array 220 shown in FIG. 2 is a two-dimensional 8 × 8 array of 64 (N = 64) discrete aperture elements, where each element of the array 220 is tabular “[row, column ] "Is arranged in a two-dimensional row and column format so that it can be uniquely identified using the notation. Thus, by way of example, the first element in the first row of array 220 is referenced as 220 [1,1], and the last element in the last row of array 220 is referenced as 220 [8,8].

  In practice, the size and format of the array 220 may have much more (or less) elements than this, depending on the desired resolution of the image 140. By way of example only, the array 220 may be an array of 640 × 480 (N = 307200) elements for the desired image resolution 640 × 480 pixels of the image 140, or for a higher desired resolution of the image 140. It may be an array of elements of 1920 × 1080 (N = 2073600).

  The overall transmission at a given time of the light 105 that passes through the array 220 and reaches the sensor 230 can be varied by setting the transmittance of one or more individual aperture elements of the array. For example, the transmittance of one or more elements from aperture elements 220 [1,1] to 220 [8,8] can be selectively and individually changed to pass through array 220 and sensor 230 at a given time. By increasing or decreasing the amount of light 105 reaching, the overall transmittance of the array 220 can be adjusted.

  An aperture element that is fully open (eg, fully enabled or activated) allows light 105 to pass through the open element to reach sensor 230, but is completely closed An aperture element (eg, completely disabled or deactivated) prevents or blocks light 105 from passing through the closed elements of its array 220 to reach the photon detector 230. The aperture element is partially open (or partially closed), allowing only some but not all of the light 105 to pass through to the sensor 230 via the partially open (or partially closed) element. You may make it reach. Thus, the collective state of the individual aperture elements (eg, open, closed, or partially open or closed) determines the overall transmittance of the aperture array 220 and, therefore, a given The amount of light 105 reaching the sensor 230 at this time is determined.

  In one embodiment, the aperture array 220 is a micromirror array of N micromirrors that can be individually selected. In another embodiment, the aperture array 120 may be an N-element LCD array. In another embodiment, the aperture array 220 may be any suitable array of electronic or optical components whose transmission can be selectively controlled.

The camera unit 115 is configured to generate an intermediate compression measurements by adjusting the overall transmittance of the aperture array 220 in accordance with a compression base information in multiple sequence matrix selectively. Each intermediate compression measurements, some specific of the N-port element of the array 220, selectively according to the pattern indicated by the specific compression basis of a sequence matrix 1 20 (completely or partially) It may be understood as the determined sum (or total) of the light 105 that reaches the sensor 230 through the array 220 during a particular time when it is open or closed.

One feature of the present disclosure is to use the respective S-number of sequence matrix that is determined as described in detail below, intermediate compression measurements of the M is obtained. Since S ≧ 2, at least 2M intermediate compression measurements are determined and processed to produce M compression measurements 125 representing a compressed image of the object 110, as described in detail below. Is done. Using the M compressed measurements 125 with the reconstruction matrix 150, a reconstructed image 140 of the object 110 is reconstructed or generated. Another feature of the present disclosure are determined sequence matrix based on a certain kernel function is that its kernel function is determined based on the attributes of the array 220 and the sensor 230. These and other aspects of the disclosure are described in detail below.

In general, determined sequence matrix 1 20 compression of the M basis b _1, b 2, a set of _... b _M, then each of which is applied to the array 220, the M intermediate compression measurements Each one is generated. Each measurement basis b _1, b 2 of the sequence matrix 1 in 20, _... b _M is itself a sequence of N values corresponding to the number N of the aperture element of the array 220, the next mathematically It is shown as the formula.

B For example, in the embodiment shown in FIG. 2, from each of the compressed base _b k in a given sequence matrix 1 20 (k∈ [1 ... M ]), as described later, the value _b k [1] a set of _k [64], each value of which is normalized to the set [0,1]. Thus, each value of a given compression basis may be “0”, “1”, or a real number between “0” and “1”, each of which is an 8 × 8 aperture array The corresponding state (eg, fully closed, fully open, or intermediate state) of each aperture element in 220 is determined.

Applying a given compression basis b _k to the array 220, a corresponding intermediate compression measurement for a time t _k is generated as follows. Using the respective values from b _k [1] to b _k [64], the state of the corresponding element of array 220 (fully open, fully closed, or partially open or closed) is set, and sensor 230 is The detected sum or total of the incoming light 105 is determined as the value of the corresponding intermediate compression measurement. In this way, a total of M × S intermediate compression measurements are generated. Where M is the number of compression base of each sequence matrix 1 20, S is the number of sequence matrix (here, S ≧ 2).

An example operation of the system 100 will be described in connection with the process 300 of FIG. As an overview to assist the reader, 308 from step 302 will be described for the determination of the sequence matrix 1 20. Step 310, from the intermediate compression measurements obtained using the sequence matrix 1 20, it will be described by determining the compression measurements 125 representing a compressed image of the object 110. Step 312 describes generating a reconstructed image of the object 110 from the compressed measurements 125 using the reconstruction matrix 150.

  The steps described below are for illustrative purposes only, steps present here may be modified or omitted, additional steps may be added, and the order of certain steps may be changed. Should be understood.

Referring to process 300 of FIG. 3, the determination of the sequence matrix 1 20, at step 302, it begins with the calculation of the N × N kernel matrix K. The kernel matrix K is determined based on the geometry and attributes of the array 220 and sensor 230. The kernel matrix may be determined as follows.

  In one embodiment, the kernel matrix is calculated based on the sensitivity function of sensor 230 and the characteristic function of array 220. First, the sensitivity function F (x, y) of the sensor 230 is determined. Here, F (x, y) is a response of the sensor 230 when light hits the point x, y of the orthogonal coordinates on the sensor. Preferably, sensor 230 has a wide sensing area and uniform (or nearly uniform) so that the sensor response (in other words, sensor sensitivity) does not vary (or does not vary much) depending on where the light strikes the sensor. Is selected to have a sensitivity function F (x, y), but this is not essential.

  Next, the characteristic function E (x, y) of a given aperture element E is such that E (x, y) = 1, point x, y when the Cartesian coordinate point x, y is within the aperture element region. Is outside the aperture element region, a characteristic function is determined for each aperture element in the array so that E (x, y) = 0.

として定められる。ここで、Ｅ_{ｒｏｗ，ｃｏｌｕｍｎ}は、行列表記を使用して配列２２０の特定の開口素子Ｅを識別する。 Next, using the sensitivity function of the sensor 230 and the characteristic function of the aperture elements of the array 220, the kernel function k (x, y) is defined as k (x, y) = E * F. Here, the operator * indicates a two-dimensional (2D) convolution operation. The discrete kernel function k (row, column) is

It is determined as Here, E _{row, column} identifies a particular aperture element E of array 220 using matrix notation.

  Alternatively, in another embodiment, the discrete kernel function is obtained by calibrating the camera unit 115 using a point light source (eg, a laser light source or another light source that is substantially a point light source relative to the camera unit 115). Note that it may be obtained.

Finally, an N × N kernel matrix K is calculated from the discrete kernel function as K · I _1D = (k (row, column) * I _2D ) _1D . Here, 1D indicates a one-dimensional (1D) vector format of a 2D array, and I is an arbitrary N pixel image.

In step 304, determination of the sequence matrix 1 20 by specifying the reconstruction matrix 150 to continue. The reconstruction matrix may be any M × N matrix with attributes suitable for use in compressed sensing imaging, such as limited isometricity. In one embodiment, therefore, reconstruction matrix 150 has the known attribute that such reconstruction matrix term or value is either +1 or -1 and the rows are orthogonal to each other. It may be an M × N matrix in which rows are selected from randomly or pseudo-randomly sorted N × N Hadamard matrices.

In step 306, determination of the sequence matrix 1 20 continues by calculating the sensing matrix A of M × N. Here, the sensing matrix is calculated as A = [a _ij ] = RK ⁻¹ , R is the M × N reconstruction matrix calculated in step 304, and K ⁻¹ is the sensor 230 and the array in step 302. Is an M × N inverse matrix of an N × N kernel matrix K determined based on 220 attributes, and [a _ij ] is the sensing matrix A for i = 1,..., M and j = 1,. Value.

Although the sensing matrix A is M × N matrix determined based on the attributes of the array 220 and sensor 230, it is not suitable for use directly as a sequence matrix 1 20 is indicated. The reason is that one or more values [a _ij ] of the sensing matrix A do not satisfy 0 ≦ a _{i, j} ≦ 1, as is apparent from at least the negative values of the reconstruction matrix R. In fact, the sensing matrix A may contain large negative and positive values, which are impractical (or possibly used) to use as a pattern for setting the aperture element conditions of the array 220. Impossible).

As a result, in step 308, it is further decomposed sensing matrix A, as described below, the sequence matrix 1 20 with a value that is in the set [0, 1]. The following description, when it corresponds to the case to have a value in the set [0, 1] in the sequence matrix, the following disclosure is to degrade the sensing matrix A to have a value in the other set Note that is also applicable.

For a given sensing matrix A, we define:

に分解される。ここで、ｉ＝１，…，Ｍ、ｊ＝１，…，Ｎ、ｋ＝１，…，Ｐ^＋である。

Then, by using the pseudo code algorithm below, A ⁺ is P ⁺ number of M × N sequence matrix

Is broken down into Here, i = 1, ..., M , j = 1, ..., N, k = 1, ..., a ^{P +.}

となることができる。ここで、ｉ＝１，…，Ｍ、ｊ＝１，…，Ｎ、ｋ＝１，…，Ｐ⁻である。 Then, based on the above algorithm, the matrix A ^- is similarly decomposed, P ^- number of M × N sequence matrix

Can be. Here, i = 1, ..., M , j = 1, ..., N, k = 1, ..., P - is.

の値がすべて

を満たし、同様に、結果のＰ⁻個のＭ×Ｎシーケンス行列

のそれぞれの全ての値もまた

を満たすことに留意されたい。 The results of the ^{P +} number of M × N sequence matrix

All values

The filled, likewise, results P ^- number of M × N sequence matrix

All the values of

Please note that

When the sensing matrix is decomposed into the above sequence matrix, the following equation is obtained.

および

がそれぞれ配列２２０に提供されて、前述したように、中間圧縮測定値が得られる。例えば、一実施形態では、Ｍ×Ｎシーケンス行列

がそれぞれ配列２２０に適用され、対応するＭ個の中間圧縮測定値のセットの測定値ベクトル

が生成される。同様に、Ｍ×Ｎシーケンス行列

のそれぞれもまた配列２２０に適用され、対応するＭ個の中間圧縮測定値の測定値ベクトル

が生成される。 In step 310, the determined sequence matrix

and

Are each provided in array 220 to obtain intermediate compression measurements as described above. For example, in one embodiment, M × N sequence matrix

Are applied to array 220, respectively, and a measurement vector of a corresponding set of M intermediate compression measurements

Is generated. Similarly, M × N sequence matrix

Each of which is also applied to the array 220 and is a measurement vector of the corresponding M intermediate compression measurements.

Is generated.

  At step 312, the process determines a compressed measurement 125 representing a compressed image of the object 110 from the intermediate compressed measurement determined at step 310 and reconstructs a decompressed image of the object 110 from the compressed measurement 125. Including.

および

を使用して、次のとおり決定される。

Specifically, M compression measurement values 125 are intermediate compression measurement values.

and

Is determined as follows.

The restored image I of the object 110 may be determined using the compression measurements 125 and the reconstruction matrix 150 as follows.

Here, W is a sparse operator, I is a one-dimensional matrix representation of an image 140 having N values, R is the reconstructed basis matrix determined in step 304, y = Y ₁ , Y ₂ , Y ₃ , ... Y _M is a column vector of the compression measurements 125 obtained on the basis of the intermediate compression measurements obtained using the sequence matrix. The sparse operator W may be generated, for example, using wavelets or using total variation.

  The process steps 304 to 312 described above may be repeated for one image or video frame or may be performed once. Step 302 need not be repeated unless another kernel matrix K is desired, for example if there is a change in array 220 or sensor 230.

  The present disclosure is believed to provide a number of advantages. First, the present disclosure describes an improved lensless camera unit suitable for compressed sensing imaging, which camera unit uses a number of measurements using array 220 to generate M compressed measurements. Provide higher quality images in low light with high signal-to-noise ratio due to the acquisition of values (at least 2 × M). Furthermore, the measured values are obtained taking into account the aperture array and certain attributes of the sensor. Subsequently, the present disclosure is suitable for images of the full spectrum of light including visible and invisible spectra. In addition, the present disclosure provides for relatively large sensors and known for producing a soft (relatively blurred) image for a given geometry and size of the sensor, particularly in other ways. A method is also provided for capturing a clearer (eg, having a large amount of detail) image of the aperture array.

  It is understood that one or more aspects of the present disclosure may be implemented using hardware, software, or a combination thereof. FIG. 4 illustrates a high-level block diagram of an example processing device or apparatus 400 suitable for implementing one or more aspects of the present disclosure. Apparatus 400 includes a processor 402 communicatively interconnected to various input / output devices 404 and memory 406.

  The processor 402 may be any type of processor, such as a general purpose central processing unit (CPU) or a dedicated microprocessor such as an embedded microcontroller or digital signal processor (DSP). The input / output device 404 operates under the control of the processor 402 and is configured to input / output data to / from the apparatus 400 according to the present disclosure, such as a lens camera or a lensless camera, or a video capture device, for example. Any peripheral device, and may include an aperture array and a sensor. The input / output device 404 may also include prior art network adapters, data ports, and various user interfaces such as a keyboard, keypad, mouse, or display.

  Memory 406 may be any type of memory suitable for storing electronic information including data and instructions executable by processor 402. The memory 406 may be implemented as one or a combination of random access memory (RAM), read only memory (ROM), flash memory, hard disk drive memory, compact disk memory, optical memory, and the like, for example. Further, apparatus 400 may also include an operating system, queue manager, device driver, or one or more network protocols, which in one embodiment are stored in memory 406 and executed by processor 402. Good.

Memory 406 may include non-transitory memory for storing executable instructions and data configured to cause apparatus 400 to perform functions in accordance with the various aspects and steps described above when executed by processor 402. May be. In some embodiments, the processor 402 is configured to communicate with, control, or implement all or part of the previously described functions related to obtaining or reconfiguring compression measurements when instructions are executed. May be. The processor sequence matrix, intermediate compression measurements, determines the compression measurements, using a reconstituted matrix determined as described previously, it may be configured to generate a restored image or video.

  In some embodiments, the processor 402 may also be configured to communicate with and / or control another device 400 that is interconnected, eg, via a network. In such cases, the functionality disclosed herein may be integrated into each of the stand-alone devices 400, or may be distributed among one or more devices 400. In some embodiments, the processor 402 may also be configured as a plurality of interconnected processors that are located at separate locations and communicatively interconnected with each other (eg, within a cloud computing environment). .

  Although a particular device configuration is shown in FIG. 4, it is understood that the present disclosure is not limited to any particular implementation. For example, in some embodiments, all or part of the functionality disclosed herein uses one or more application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), etc. May be implemented.

  Although aspects of this specification have been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present disclosure. Accordingly, it should be understood that many modifications can be made to these illustrative embodiments and that other configurations can be devised without departing from the spirit and scope of the disclosure.

Claims (7)

A compressed sensing imaging system,
A processing device, the processing device comprising:
Determine the sensitivity function of the sensor,
Determining at least one characteristic function for at least one of the plurality of aperture elements of the aperture array;
Calculating a kernel function by performing a convolution operation using the sensitivity function and the at least one characteristic function;
Determine a kernel matrix using the kernel function and the image;
Generating a sensing matrix using the kernel matrix and the reconstructed basis matrix, wherein the reconstructed basis matrix has attributes suitable for use in compressed sensing imaging;
Decomposing the sensing matrix to generate a plurality of sequence matrices;
Configured to generate a plurality of compressed measurements representing a compressed image of the object using the plurality of sequence matrices;
Compressed sensing imaging system.   The compressed sensing imaging system of claim 1, wherein the processing device is further configured to generate a reconstructed image of the object from the plurality of compressed measurements using the reconstructed basis matrix.   The processing device is further configured to apply a sparse operator to generate a reconstructed image of the object from the plurality of compressed measurements using the reconstructed basis matrix. Compressed sensing imaging system.   The compression sensing imaging system according to claim 1, further comprising a lensless camera unit including an aperture array composed of aperture elements and a sensor for detecting light passing through the aperture elements of the aperture array. The processing device is
Selectively enabling or disabling one or more of the aperture elements of the aperture array based on at least one basis in a sequence matrix during a period of time, wherein at least a plurality of intermediate compression measurements Get one,
Using said plurality of intermediate compression measurements, the further configured to generate the plurality of compression measure representing a compressed image of the object, at least one of said plurality of intermediate compression measurements, during the period The compressed sensing imaging system of claim 4, wherein the compression sensing imaging system is determined based on a grand total of light detected by the sensor.   The compressed sensing imaging system according to claim 4, wherein the aperture array is a micromirror array.   The compressed sensing imaging system of claim 4, wherein the aperture array is an LCD array.

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