Modulation transfer function evaluation of red / transparency filtered optical devices

Abstract

Modulation transfer function evaluation of red / transparent filtered optical devices. Systems and methods for evaluating the modulation transfer function (MTF) of a device under test including a red / transparent color filter are provided. A pattern source provides a pattern. The device under test images the pattern to provide an image including a plurality of filtered pixels and a plurality of unfiltered pixels, each filtered and unfiltered pixel having an associated luminance value. An image analysis system determines, for each filtered pixel, one of a plurality of axes having a lowest curvature. A first axis of the plurality of axes is not orthogonal to a second axis. An interpolated image having new luminance values for each of the plurality of filtered pixels is generated, the new luminance value being a function of respective luminance values of a set of unfiltered pixels selected from a first order Moore neighborhood of pixels along the axis having the lowest curvature. The modulation transfer function is determined from the interpolated image.

Description

TECHNICAL FIELD

[0001] The present invention relates to optical devices, and more particularly, to a modulation transfer function evaluation system for optical devices. BACKGROUND

[0002] The modulation transfer function ("MTF") of a camera is a measure of the camera's ability to transfer contrast from an imaged object to a camera image at a particular resolution. The MTF is a way to incorporate resolution and contrast into a single specification. The MTF is one of the best tools available to quantify the overall imaging performance of a camera system in terms of resolution and contrast. Thus, knowing the MTF of each imaging lens and camera sensor within a system allows a designer to make proper choices when optimizing a camera system for a particular resolution. SUMMARY

[0003] According to an aspect of the present invention, a system for evaluating a modulation transfer function (MTF) of a device under test including a red / clear (RCCC) color filter is provided. A pattern source provides a pattern suitable for evaluating the MTF by a slanted method. The device under test images the pattern to provide an image including a plurality of filtered pixels and a plurality of unfiltered pixels, each filtered pixel having an associated luminance value, each unfiltered pixel having an associated luminance value. An image analysis system determines, for each filtered pixel of the plurality of filtered pixels, one of a plurality of axes having a lowest curvature. A first axis of the plurality of axes is not orthogonal to a second axis of the plurality of axes. An interpolated image having new luminance values for each filtered pixel of the plurality of filtered pixels is generated as a function of respective luminance values of a set of unfiltered pixels selected from a first order Moore neighborhood of the pixel. The set of pixels is selected along the axis having the lowest curvature. The modulation transfer function is determined from the interpolated image.

[0004] According to another aspect of the application, there is provided a method for evaluating a modulation transfer function (MTF) of a device under test comprising a red / clear (RCCC) color filter. A pattern is imaged with the device under test to provide an image comprising a plurality of filtered pixels and a plurality of unfiltered pixels, each filtered pixel having an associated luminance value, each unfiltered pixel having an associated luminance value. For each filtered pixel of the plurality of filtered pixels, one of a plurality of axes having a lowest curvature is determined. A first axis of the plurality of axes is not orthogonal to a second axis of the plurality of axes. An interpolated image having new luminance values for each filtered pixel of the plurality of filtered pixels is generated as a function of respective luminance values of a set of unfiltered pixels selected from a first order Moore neighborhood of the pixel along the axis having the lowest curvature. The modulation transfer function is determined from the interpolated image. BRIEF DESCRIPTION OF DRAWINGS

[0005] Figure 1 An example of a system for evaluating a modulation transfer function of a device under test comprising a red / clear (RCCC) color filter is shown;

[0006] Figure 2 An example of a second order Moore neighborhood around a pixel is shown;

[0007] Figure 3 A method for evaluating a modulation transfer function (MTF) of a device under test comprising a red / clear (RCCC) color filter is shown; and

[0008] Figure 4 is a schematic block diagram showing an example system of hardware components capable of implementing Figures 1 to 3 the systems and methods disclosed in DETAILED DESCRIPTION

[0009] One method of determining a modulation transfer function ("MTF") of a camera is to place a set of targets at the hyperfocal distance of the camera. In one embodiment of this method, printed targets are mounted to a wall. There are a total of 11 targets, one of which is used for on-axis measurements and the other 10 are used for off-axis measurements. The device under test ("DUT") and the targets are separated by the hyperfocal distance of the camera and the wall targets are illuminated by an LED lighting panel. The reflected light from the targets is captured by the DUT and metrics are measured and used to provide MTF scores for the available slanted edge measured by the module. In one embodiment, a slanted edge technique in compliance with the ISO 12233 standard is employed to provide a way to quickly and effectively measure the modulation transfer function (MTF) of a digital input device using a normalized reflection target based on the slanted edge method.

[0010] The slanted edge MTF technique is a kind of edge gradient MTF method, particularly suitable for MTF calculation for spatially sampled capture devices. Its main feature is to intelligently create 1-D uniformly oversampled edge profiles from consecutive lines of a 2-D raw sampled image, whose line-to-line edge positions are slightly shifted from each other, as if it would have a slanted edge. In theory, this allows for a definite MTF estimation beyond the Nyquist frequency of the capture device, which is always the limiting factor of a sampled device. Another claimed advantage is its alignment insensitivity. In fact, the method requires edge misalignment in order to perform oversampling. Unfortunately, the slanted edge method assumes monochromatic images, and the use of the method performed with a device having a color filter array, e.g., using a red / clear (RCCC) filter array, requires interpolation of filtered pixel values in the array.

[0011] Figure 1 An example of a system 100 for evaluating the modulation transfer function of a device under test 112 including a red / clear (RCCC) color filter is shown. The system 100 includes a pattern source 114 configured to provide a pattern representing a desired image. In one embodiment, the pattern source 114 is a printed target. In another embodiment, the pattern source 114 includes a suitable coherent or incoherent light source passing through a transparent plate having a pattern of opaque or translucent material thereon to provide the desired pattern. For example, the light source can include a laser of a narrow band but already collimated light source or a non-coherent light source having a wider spectrum provided at a desired wavelength. Alternatively, the pattern source 114 can represent a display such as an LCD display provided with appropriate input to display one or more targets.

[0012] The device under test 112 images the pattern provided by the pattern source 114 to provide an image having a plurality of pixels, each pixel represented by an associated luminance value representing the light intensity measured at the pixel. Due to the RCCC filter, the image includes a plurality of unfiltered pixels as well as a plurality of filtered pixels in which only the red content of the incident light is represented. At least one image from the device under test 112 is provided to an image analysis system 120. It should be appreciated that the image analysis system 120 can be implemented as machine readable instructions stored on a non-transitory computer readable medium and executed by an associated processor, as dedicated hardware such as a field programmable gate array or an application specific integrated circuit, or as a combination of software and dedicated hardware.

[0013] The image analysis system 120 is configured to compute the MTF of the device under test 112 from at least one image taken at the device under test 112. The image analysis system 120 includes an axis selector 122 that determines, for each filtered pixel of the plurality of filtered pixels, one of a plurality of axes having a lowest curvature, i.e., a second derivative of the luminance values at the pixel. At least one of the plurality of axes is not orthogonal to another of the plurality of axes. In one example, the plurality of axes includes a horizontal axis, a vertical axis, and two diagonal axes that pass through the pixel. To estimate the curvature along each axis, the luminance values of a set of multiple filtered pixels within a second Moore neighborhood of the pixel can be combined to provide an estimated curvature for each of the plurality of axes.

[0014] Figure 2 An example of a second Moore neighborhood 200 around a pixel 202 is shown. The pixel 202 and eight additional pixels 204-211 along the edges of the second Moore neighborhood 200 of the pixel are filtered pixels, shown as shaded in the figure. Each of the plurality of axes 214-217 passes through the pixel 202 and two of the filtered pixels 204-211, and the local curvature of the image along each axis 214-217 is determined from the pixels along the axis. In particular, the estimate of the curvature along each axis can be determined as the difference between the sum of the luminance values CI and C2 of the two pixels along the edge of the second Moore neighborhood (e.g., 204 and 211) and twice the luminance value C of the pixel 202, such that the curvature of the axes 214-217 is estimated as CI + C2 - 2C p p When the computation of the curvature is only used for comparison to find the axis with the lowest estimated curvature, the final term can be omitted because the value of the central pixel 202 is the same for all axes 214-217. Thus, for purposes of comparison, the estimate of the curvature can be determined as CI + C2 for these axes.

[0015] The image generator 124 generates an interpolated image having new luminance values for each of the plurality of filtered pixels as a function of the respective luminance values of a set of unfiltered pixels selected from a first Moore neighborhood of the pixel along the axis with the lowest curvature. With reference to Figure 2 ​The first-order Mohs neighborhood of pixel 202 includes the pixel itself and pixels 221-228, all of which are unfiltered pixels. The interpolated image contains a new luminance value for each of the plurality of filtered pixels, the new luminance value being generated as a function of the corresponding luminance values ​​of a set of unfiltered pixels selected from the first-order Mohs neighborhood, and more specifically as a function of the luminance values ​​of two pixels adjacent to the pixel along a selected axis. In one embodiment, the new value is generated as the arithmetic mean of the luminance values ​​of two unfiltered pixels (e.g., 221 and 228) along a selected axis (e.g., 214).

[0016] MTF calculator 126 determines the modulation transfer function of device under test 112 based on the interpolated image. In one example, for a selected region of interest in the target image, MTF calculator 126 uses a slanted edge method to generate the MTF. In this embodiment, MTF calculator 126 estimates the edge position of each scan line in a plurality of scan lines, generates a best-fit line passing through the center of the set of edge positions using a regression method, registers each line based on the regression fit, distributes the line data into uniformly sampled bins, takes the derivative of the binned data to generate a line spread function, performs a discrete Fourier transform on the windowed portion of the line spread function (e.g., using a Hamming window), and calculates the modulation transfer function based on the discrete Fourier transform. The determined MTF is then provided to the user via user interface 130. User interface 130 may include an output device such as a display, and appropriate software for interacting with the system via one or more input devices such as a mouse, keyboard, or touchscreen.

[0017] In view of the above Figure 1 and Figure 2 The aforementioned structural and functional features described herein will be referenced. Figure 3 To better understand the example methods. Although for the purpose of simplifying the explanation, Figure 3 The methods are shown and described as being performed sequentially, but it should be understood and recognized that the invention is not limited to the order shown, as in other instances some actions may occur in a different order and / or simultaneously with the order shown and described herein.

[0018] Figure 3A method 300 for evaluating the modulation transfer function (MTF) of a device under test (DUT) including a red / clear (RCCC) color filter is illustrated. At 302, a pattern is imaged using the DUT to provide an image comprising a plurality of filtered pixels and a plurality of unfiltered pixels, each filtered pixel having an associated luminance value and each unfiltered pixel having an associated luminance value. At 304, for each of the plurality of filtered pixels, one of a plurality of axes having the lowest curvature is determined. At least a first axis of the plurality of axes forms an oblique angle with a second axis of the plurality of axes. In one example, for each filtered pixel, the axis having the lowest curvature comprises a set of luminance values ​​of the plurality of filtered pixels within a second-order Moiré neighborhood of the combined pixels to provide an estimated curvature for each of the plurality of axes, and the axis having the lowest estimated curvature is selected. For example, each group may include a first filtered pixel separated from the pixel by one pixel in a first direction and a second filtered pixel separated from the pixel by one pixel in a second direction directly opposite to the first direction, and the estimated curvature of each axis may be determined as the difference between the sum of the luminance values ​​of the first filtered pixel and the luminance values ​​of the second filtered pixel and twice the pixel luminance value.

[0019] At 306, an interpolated image is generated. The interpolated image has a new luminance value for each of the plurality of filtered pixels, the new luminance value being a function of a set of unfiltered corresponding luminance values ​​selected from a first-order Mohr's neighborhood of the pixel chosen along the axis with the lowest curvature. For example, the new luminance value can be determined as the arithmetic mean of two pixels adjacent to the pixel along the selected axis. Then, at 308, a modulation transfer function is determined based on the interpolated image. In one example, the modulation transfer function is determined based on the interpolated image using a slanted edge method.

[0020] Figure 4 This is a schematic diagram illustrating the feasibility of implementation. Figures 1 to 3 An exemplary system 400 of hardware components, such as instances of the systems and methods disclosed herein. Figure 1 The image analysis system 120 shown is illustrated. System 400 can include various systems and subsystems. System 400 can be a personal computer, laptop computer, workstation, computer system, electrical appliance, application-specific integrated circuit (ASIC), server, server blade center, server cluster, etc.

[0021] The system 400 can include a system bus 402, a processing unit 404, a system memory 406, memory devices 408 and 410, a communication interface 412 (e.g., a network interface), a communication link 414, a display 416 (e.g., a video screen), and an input device 418 (e.g., a keyboard and / or a mouse). The system bus 402 can be in communication with the processing unit 404 and the system memory 406. Additional memory devices 408 and 410, such as hard disk drives, servers, stand-alone databases, or other non-volatile memory, can also be in communication with the system bus 402. The system bus 402 interconnects the processing unit 404, the memory devices 406-410, the communication interface 412, the display 416, and the input device 418. In some examples, the system bus 402 also interconnects additional ports (not shown), such as universal serial bus (USB) ports.

[0022] The processing unit 404 can be a computing device and can include an application specific integrated circuit (ASIC). The processing unit 404 executes a set of instructions to implement the operations of the examples disclosed herein. The processing unit can include a processing core.

[0023] The additional memory devices 406, 408, and 410 can store data, programs, instructions, database queries, and any other information needed to operate a computer, in text or compiled form. The memory 406, 408, and 410 can be implemented as computer-readable media (integrated or removable), such as a memory card, a disk drive, a compact disk (CD), or a server accessible over a network. In certain examples, the memory 406, 408, and 410 can include text, images, video, and / or audio, some of which can be available in a human understandable format. Additionally or alternatively, the system 400 can access external data sources or query sources through the communication interface 412, which can be in communication with the system bus 402 and the communication link 414.

[0024] In operation, the system 400 can be used to implement one or more portions of an organizational screening system according to the present disclosure. According to certain examples, computer executable logic for implementing an organizational screening system resides on one or more of the system memory 406 and the memory devices 408, 410. The processing unit 404 executes one or more computer executable instructions sourced from the system memory 406 and the memory devices 408 and 410. As used herein, the term "computer readable medium" refers to any medium that participates in providing instructions to the processing unit 404 for execution, and it is to be appreciated that a computer readable medium can include a plurality of computer readable media, each operatively connected to the processing unit.

[0025] Also, it should be noted that embodiments can be described as a process which is depicted as a flowchart, flow diagram, data flow diagram, structure diagram, or block diagram. Although a flowchart can describe operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations can be rearranged. A process is terminated when its operations are completed. A process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.

[0026] Furthermore, embodiments can be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and / or any combination thereof. When implemented in software, firmware, middleware, scripting language, and / or microcode, the program code or code segments to perform the necessary tasks can be stored in a machine readable medium such as a storage medium. A code segment or machine-executable instruction can represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures, and / or program statements. A code segment can be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, and / or memory contents. Information, arguments, parameters, data, etc. can be passed, forwarded, or transmitted using any suitable means including memory sharing, message passing, ticket passing, network transmission, etc.

[0027] For firmware and / or software implementations, the methodologies can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. Any machine readable medium tangibly embodying instructions can be used in implementing the methodologies described herein. For example software codes can be stored in memory. Memory can be implemented within the processor or external to the processor. As used herein the term "memory" refers to any type of long term, short term, volatile, nonvolatile, or other memory and is not to be limited to any particular type of memory or number of memories or type of media upon which memory is stored.

[0028] Also, as disclosed herein, the term "storage medium" can represent one or more memories for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices, and / or other machine readable mediums for storing information. The term "machine-readable medium" includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels, and / or various other storage mediums capable of storing that contain or carry instruction(s) and / or data.

[0029] What has been described above are examples of the present application. Of course, it is not possible to describe every conceivable combination of components or methodologies for purposes of describing the present application, but one of ordinary skill in the art will recognize that many other combinations and permutations of the present application are possible. Although certain novel features of the present application shown and described are pointed out in the claims that follow, the application is not to be limited to the features specified as such, since the claims include all novel equivalents of the features shown and described. Therefore, the application is intended to encompass all such changes, modifications and variations which fall within the scope of the appended claims. As used herein, the terms "includes" means includes but is not limited to, the term "including" means including but not limited to. The term "based on" means based at least in part on. Additionally, where the disclosure or claims recite "a," "an," "the" or "another" element or equivalent thereof, those are terms used to introduce the described element in a non-exclusive sense, that is, the use of these terms does not, by itself, rule out the presence of more than one of the described element, whether such more than one of the described element is referred to in the claims or not. No feature of the application is key or essential unless explicitly so stated.

Claims

1. A system for evaluating a modulation transfer function (MTF) of a device under test comprising a red / clear (RCCC) color filter, the system comprising: a pattern source providing a pattern suitable for evaluating the MTF by a tilt method; the device under test imaging the pattern to provide an image comprising a plurality of filtered pixels and a plurality of unfiltered pixels, each filtered pixel having an associated luminance value, each unfiltered pixel having an associated luminance value; and an image analysis system determining, for each filtered pixel of the plurality of filtered pixels, an axis of the plurality of axes having a lowest curvature, wherein a first axis of the plurality of axes is not orthogonal to a second axis of the plurality of axes, thereby generating an interpolated image having a new luminance value for each filtered pixel of the plurality of filtered pixels as a function of respective luminance values of a set of unfiltered pixels selected from a first order Moore neighborhood of the pixel, the set of unfiltered pixels being selected along the axis having the lowest curvature, and determining the modulation transfer function from the interpolated image.

2. The system of claim 1, wherein the image analysis system comprises a processor and a non-transitory computer readable medium storing executable instructions executable by the processor.

3. The system of claim 1, further comprising a user interface displaying the determined modulation transfer function to a user.

4. The system of claim 1, wherein the image analysis system determines the axis having the lowest curvature for each filtered pixel of the plurality of filtered pixels by combining luminance values of a set of the plurality of filtered pixels within a second order Moore neighborhood of the pixel to provide an estimated curvature for each axis of the plurality of axes and selecting the axis having the lowest estimated curvature.

5. The system of claim 4, wherein the set of the plurality of filtered pixels within the second order Moore neighborhood of the pixel comprises a first filtered pixel separated from the pixel by one pixel in a first direction and a second filtered pixel separated from the pixel by one pixel in a second direction directly opposite the first direction.

6. The system of claim 5, wherein the image analysis system calculates the estimated curvature as a difference between a sum of a luminance value of the first filtered pixel and a luminance value of the second filtered pixel and twice a luminance value of the pixel.

7. The system of claim 1, wherein the image analysis system generates the new luminance value for each filtered pixel of the plurality of filtered pixels as a function of two pixels adjacent to the pixel along the axis having the lowest curvature.

8. A method for evaluating a modulation transfer function (MTF) of a device under test comprising a red / clear (RCCC) color filter, the method comprising: imaging the pattern with the device under test to provide an image comprising a plurality of filtered pixels and a plurality of unfiltered pixels, each filtered pixel having an associated luminance value, each unfiltered pixel having an associated luminance value; determining, for each filtered pixel of the plurality of filtered pixels, an axis of the plurality of axes having a lowest curvature, wherein a first axis of the plurality of axes is not orthogonal to a second axis of the plurality of axes; and generating an interpolated image having, for each filtered pixel of the plurality of filtered pixels, a new luminance value as a function of respective luminance values for a set of unfiltered pixels selected from a first-order Moore neighborhood of the pixel, the set of unfiltered pixels being selected along the axis having the lowest curvature; and determining the modulation transfer function from the interpolated image.

9. The method of claim 8, wherein the determining, for each filtered pixel of the plurality of filtered pixels, the axis having a lowest curvature comprises: combining luminance values of a set of the plurality of filtered pixels within a second-order Moore neighborhood of the pixel to provide an estimated curvature for each axis of the plurality of axes; and selecting the axis having a lowest estimated curvature.

10. The method of claim 9, wherein the combining luminance values of the set of the plurality of filtered pixels within the second-order Moore neighborhood of the pixel comprises selecting, for each set, a first filtered pixel separated from the pixel by one pixel in a first direction and a second filtered pixel separated from the pixel by one pixel in a second direction directly opposite the first direction.

11. The method of claim 10, wherein the combining luminance values of the set of the plurality of filtered pixels within the second-order Moore neighborhood comprises calculating a difference between a sum of a luminance value of the first filtered pixel and a luminance value of the second filtered pixel and twice a luminance value of the pixel.

12. The method of claim 10, wherein the generating, for each filtered pixel of the plurality of filtered pixels, a new luminance value as a function of respective luminance values for the set of unfiltered pixels comprises determining an arithmetic mean of two pixels adjacent to the pixel along the selected axis.

13. The method of claim 8, wherein the plurality of axes includes a vertical axis, a horizontal axis, a first diagonal axis, and a second diagonal axis.

14. The method of claim 8, wherein the generating, for each filtered pixel of the plurality of filtered pixels, a new luminance value as a function of respective luminance values for the set of unfiltered pixels comprises selecting the set of unfiltered pixels as two pixels adjacent to the pixel along the axis having the lowest curvature.

15. The method of claim 14, wherein generating a new luminance value for each filtered pixel of the plurality of filtered pixels as a function of a respective luminance value for the set of unfiltered pixels comprises calculating an arithmetic mean of the luminance values of the set of unfiltered pixels.

Images