Source: http://www.patentsencyclopedia.com/app/20130250125
Timestamp: 2019-04-20 20:33:11+00:00

Document:
Various techniques are provided to capture one or more thermal image frames using an infrared sensor array that is fixably positioned to substantially de-align rows and columns of infrared sensors. In one example, an infrared imaging system includes an infrared sensor array comprising a plurality of infrared sensors arranged in rows and columns and adapted to capture a thermal image frame of a scene exhibiting at least one substantially horizontal or substantially vertical feature. The infrared imaging system also includes a housing. The infrared sensor array is fixably positioned within the housing to substantially de-align the rows and columns from the feature while the thermal image frame is captured.
1. An infrared imaging system comprising: an infrared sensor array comprising a plurality of infrared sensors arranged in rows and columns and adapted to capture a thermal image frame of a scene exhibiting at least one substantially horizontal or substantially vertical feature; a housing; and wherein the infrared sensor array is fixably positioned within the housing to substantially de-align the rows and columns from the feature while the thermal image frame is captured.
2. The infrared imaging system of claim 1, wherein: the housing comprises a surface substantially parallel to the feature; and the infrared sensor array is fixably positioned within the housing to substantially de-align the rows and columns from the surface while the thermal image frame is captured.
3. The infrared imaging system of claim 2, wherein the surface is a substantially planar external surface of the housing.
4. The infrared imaging system of claim 1, wherein the infrared sensor array is rotationally offset relative to the housing.
5. The infrared imaging system of claim 1, wherein the infrared sensor array is rotationally offset about an optical axis of the infrared imaging system.
6. The infrared imaging system of claim 1, wherein: the captured thermal image comprises rows and columns of pixels substantially de-aligned from the feature; and the infrared imaging system further comprises a processing device adapted to: calculate, for each pixel, a difference between a center pixel and a plurality of neighboring pixels to determine column neighbor differences, generate, for each column of pixels, a histogram of the column neighbor differences, use the column neighbor differences to determine column noise offset terms for corresponding columns of pixels, and apply column noise filter correction to the captured image based on the column noise offset terms, wherein the column noise offset term is applied to at least a majority of the pixels in each corresponding column.
7. The infrared imaging system of claim 1, wherein the thermal image frame is an unblurred thermal image frame, the infrared imaging system further comprising a processing device adapted to: receive an intentionally blurred thermal image frame; process the blurred thermal image frame to determine a plurality of non-uniformity correction (NUC) terms; and apply the NUC terms to the unblurred thermal image frame.
8. A method of operating the infrared imaging system of claim 1, the method comprising: capturing the thermal image frame while the rows and columns are substantially de-aligned from the feature.
9. An infrared imaging system comprising: an infrared sensor array comprising a plurality of infrared sensors arranged in rows and columns and adapted to capture a thermal image frame of a scene exhibiting at least one substantially horizontal or substantially vertical feature; a housing comprising a surface substantially parallel to the feature; and wherein the infrared sensor array is fixably positioned within the housing to substantially de-align the rows and columns from the surface while the thermal image frame is captured.
10. The infrared imaging system of claim 9, wherein the surface is a substantially planar external surface of the housing.
11. The infrared imaging system of claim 9, wherein the infrared sensor array is fixably positioned within the housing to substantially de-align the rows and columns from the feature while the thermal image frame is captured.
12. The infrared imaging system of claim 9, wherein the infrared sensor array is rotationally offset relative to the housing.
13. The infrared imaging system of claim 9, wherein the infrared sensor array is rotationally offset about an optical axis of the infrared imaging system.
14. The infrared imaging system of claim 9, wherein: the captured thermal image comprises rows and columns of pixels substantially de-aligned from the feature; and the infrared imaging system further comprises a processing device adapted to: calculate, for each pixel, a difference between a center pixel and a plurality of neighboring pixels to determine column neighbor differences, generate, for each column of pixels, a histogram of the column neighbor differences, use the column neighbor differences to determine column noise offset terms for corresponding columns of pixels, and apply column noise filter correction to the captured image based on the column noise offset terms, wherein the column noise offset term is applied to at least a majority of the pixels in each corresponding column.
15. The infrared imaging system of claim 9, wherein the thermal image frame is an unblurred thermal image frame, the infrared imaging system further comprising a processing device adapted to: receive an intentionally blurred thermal image frame; process the blurred thermal image frame to determine a plurality of non-uniformity correction (NUC) terms; and apply the NUC terms to the unblurred thermal image frame.
16. A method of operating the infrared imaging system of claim 9, the method comprising: capturing the thermal image frame while the rows and columns are substantially de-aligned from the feature.
17. A method comprising: capturing a thermal image frame of a scene exhibiting at least one substantially horizontal or substantially vertical feature; wherein the capturing is performed by an infrared sensor array comprising a plurality of infrared sensors arranged in rows and columns; and wherein the infrared sensor array is fixably positioned within a housing of an infrared imaging system to substantially de-align the rows and columns from the feature while the thermal image frame is captured.
18. The method of claim 17, further comprising: providing the infrared sensor array; providing the housing; and positioning the infrared sensor array within the housing to substantially de-align the rows and columns.
19. The method of claim 17, wherein: the housing comprises a surface substantially parallel to the feature; and the infrared sensor array is fixably positioned within the housing to substantially de-align the rows and columns from the surface while the thermal image frame is captured.
20. The method of claim 19, wherein the surface is a substantially planar external surface of the housing.
21. The method of claim 17, wherein the infrared sensor array is rotationally offset relative to the housing.
22. The method of claim 17, wherein the infrared sensor array is rotationally offset about an optical axis of the infrared imaging system.
23. The method of claim 17, wherein: the captured thermal image comprises rows and columns of pixels substantially de-aligned from the feature; and the method further comprises: calculating, for each pixel, a difference between a center pixel and a plurality of neighboring pixels to determine column neighbor differences, generating, for each column of pixels, a histogram of the column neighbor differences, using the column neighbor differences to determine column noise offset terms for corresponding columns of pixels, and applying column noise filter correction to the captured image based on the column noise offset terms, wherein the column noise offset term is applied to at least a majority of the pixels in each corresponding column.
24. The method of claim 17, wherein: the thermal image frame is an unblurred thermal image frame; and the method further comprises: receiving an intentionally blurred thermal image frame, processing the blurred thermal image frame to determine a plurality of non-uniformity correction (NUC) terms, and applying the NUC terms to the unblurred thermal image frame.
25. An infrared imaging system adapted to be operated in accordance with the method of claim 17.
 This patent application claims priority to and the benefit of U.S. Provisional Patent Application No. 61/646,781 filed May 14, 2012 and entitled "THERMAL IMAGE FRAME CAPTURE USING DE-ALIGNED SENSOR ARRAY," which is incorporated herein by reference in its entirety.
 This patent application is a continuation-in-part of International Patent Application No. PCT/US2012/041744 filed Jun. 8, 2012 and entitled "LOW POWER AND SMALL FORM FACTOR INFRARED IMAGING," which is incorporated herein by reference in its entirety.
 International Patent Application No. PCT/US2012/041744 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/656,889 filed Jun. 7, 2012 and entitled "LOW POWER AND SMALL FORM FACTOR INFRARED IMAGING," which are incorporated herein by reference in their entirety.
 International Patent Application No. PCT/US2012/041744 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/545,056 filed Oct. 7, 2011 and entitled "NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRARED IMAGING DEVICES," which are incorporated herein by reference in their entirety.
 International Patent Application No. PCT/US2012/041744 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011 and entitled "INFRARED CAMERA PACKAGING SYSTEMS AND METHODS," which are incorporated herein by reference in their entirety.
 International Patent Application No. PCT/US2012/041744 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun. 10, 2011 and entitled "INFRARED CAMERA SYSTEM ARCHITECTURES," which are incorporated herein by reference in their entirety.
 International Patent Application No. PCT/US2012/041744 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun. 10, 2011 and entitled "INFRARED CAMERA CALIBRATION TECHNIQUES," which are incorporated herein by reference in their entirety.
 This patent application is a continuation-in-part of International Patent Application No. PCT/US2012/041749 filed Jun. 8, 2012 and entitled "NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRARED IMAGING DEVICES," which is incorporated herein by reference in its entirety.
 International Patent Application No. PCT/US2012/041749 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/545,056 filed Oct. 7, 2011 and entitled "NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRARED IMAGING DEVICES," which are incorporated herein by reference in their entirety.
 International Patent Application No. PCT/US2012/041749 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011 and entitled "INFRARED CAMERA PACKAGING SYSTEMS AND METHODS," which are incorporated herein by reference in their entirety.
 International Patent Application No. PCT/US2012/041749 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun. 10, 2011 and entitled "INFRARED CAMERA SYSTEM ARCHITECTURES," which are incorporated herein by reference in their entirety.
 International Patent Application No. PCT/US2012/041749 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun. 10, 2011 and entitled "INFRARED CAMERA CALIBRATION TECHNIQUES," which are incorporated herein by reference in their entirety.
 This patent application is a continuation-in-part of International Patent Application No. PCT/US2012/041739 filed Jun. 8, 2012 and entitled "INFRARED CAMERA SYSTEM ARCHITECTURES," which is hereby incorporated by reference in its entirety.
 International Patent Application No. PCT/US2012/041739 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011 and entitled "INFRARED CAMERA PACKAGING SYSTEMS AND METHODS," which are incorporated herein by reference in their entirety.
 International Patent Application No. PCT/US2012/041739 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun. 10, 2011 and entitled "INFRARED CAMERA SYSTEM ARCHITECTURES," which are incorporated herein by reference in their entirety.
 International Patent Application No. PCT/US2012/041739 claims priority to and the benefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun. 10, 2011 and entitled "INFRARED CAMERA CALIBRATION TECHNIQUES," which are incorporated herein by reference in their entirety.
 This patent application is a continuation-in-part of U.S. patent application Ser. No. 13/622,178 filed Sep. 18, 2012 and entitled "SYSTEMS AND METHODS FOR PROCESSING INFRARED IMAGES," which is a continuation-in-part of U.S. patent application Ser. No. 13/529,772 filed Jun. 21, 2012 and entitled "SYSTEMS AND METHODS FOR PROCESSING INFRARED IMAGES," which is a continuation of U.S. patent application Ser. No. 12/396,340 filed Mar. 2, 2009 and entitled "SYSTEMS AND METHODS FOR PROCESSING INFRARED IMAGES," which are incorporated herein by reference in their entirety.
 One or more embodiments of the invention relate generally to thermal imaging devices and more particularly, for example, to the alignment of infrared sensors used to capture thermal image frames.
 Conventional infrared imaging devices often use arrays of infrared sensors to capture thermal images of scenes. The infrared sensors are typically implemented in rows and columns to provide generally square or rectangular arrays. Such arrays are usually positioned within imaging devices such that, when the imaging devices are positioned to capture an image of a scene, the rows of sensors are oriented substantially parallel to the ground, and the columns of sensors are oriented substantially perpendicular to the ground.
 In many environments, various features of a scene may be disposed in substantially horizontal and/or substantially vertical directions (e.g., relative to the ground or another reference plane). This is true for many manmade structures such as buildings, streets, sidewalks, and other structures. Many naturally occurring features are similarly disposed such as trees, rivers, other bodies of water, and other features. As a result, the horizontal and vertical features of an imaged scene may generally align with the rows and columns of the sensor arrays of conventional infrared imaging devices.
 Sensor arrays may exhibit various types of noise (e.g., fixed pattern noise (FPN) or others) that may be substantially correlated to rows and/or columns of infrared sensors. For example, some FPN that appears as column noise may be caused by variations in column amplifiers which may inhibit the ability to distinguish between desired vertical features of a scene and vertical FPN.
 Existing techniques used to reduce row and column noise can lead to unsatisfactory results. For example, existing noise reduction techniques may leave artifacts (e.g., image distortion or residual noise) in rows and columns of pixels of captured images. Such row and column noise artifacts may be exacerbated when features of an imaged scene are substantially aligned with rows and columns of the imager as is the case with conventionally oriented sensor arrays.
 In various embodiments, an infrared imaging system may be implemented with an infrared sensor array that is fixably positioned to substantially de-align rows and columns of infrared sensors while a thermal image frame is captured of a scene. In one embodiment, an infrared imaging system includes an infrared sensor array comprising a plurality of infrared sensors arranged in rows and columns and adapted to capture a thermal image frame of a scene exhibiting at least one substantially horizontal or substantially vertical feature; a housing; and wherein the infrared sensor array is fixably positioned within the housing to substantially de-align the rows and columns from the feature while the thermal image frame is captured.
 In another embodiment, an infrared imaging system includes an infrared sensor array comprising a plurality of infrared sensors arranged in rows and columns and adapted to capture a thermal image frame of a scene exhibiting at least one substantially horizontal or substantially vertical feature; a housing comprising a surface substantially parallel to the feature; and wherein the infrared sensor array is fixably positioned within the housing to substantially de-align the rows and columns from the surface while the thermal image frame is captured.
 In another embodiment, a method includes capturing a thermal image frame of a scene exhibiting at least one substantially horizontal or substantially vertical feature; wherein the capturing is performed by an infrared sensor array comprising a plurality of infrared sensors arranged in rows and columns; and wherein the infrared sensor array is fixably positioned within a housing of an infrared imaging system to substantially de-align the rows and columns from the feature while the thermal image frame is captured.
 FIG. 12 shows a block diagram of a system for infrared image processing, in accordance with an embodiment of the disclosure.
 FIGS. 13A-13C are flowcharts illustrating methods for noise filtering an infrared image, in accordance with embodiments of the disclosure.
 FIGS. 14A-140 are graphs illustrating infrared image data and the processing of an infrared image, in accordance with embodiments of the disclosure.
 FIG. 15 shows a portion of a row of sensor data for discussing processing techniques, in accordance with embodiments of the disclosure.
 FIGS. 16A to 16C show an exemplary implementation of column and row noise filtering for an infrared image, in accordance with embodiments of the disclosure.
 FIG. 17 illustrates an infrared imaging system with a de-aligned infrared sensor array installed in a housing in accordance with an embodiment of the disclosure.
 FIG. 18 illustrates a de-aligned infrared sensor array relative to a scene to be imaged in accordance with an embodiment of the disclosure.
 FIG. 19 illustrates a flow diagram of operations to obtain a thermal image frame using a de-aligned infrared sensor array in accordance with an embodiment of the disclosure.
 FIG. 20 illustrates a thermal image frame obtained by various operations of FIG. 19 in accordance with an embodiment of the disclosure.
 FIG. 21 illustrates a cropped thermal image frame obtained by various operations of FIG. 19 in accordance with an embodiment of the disclosure.
 Processor 195 may be implemented as any appropriate processing device (e.g., logic deice, microcontroller, processor, application specific integrated circuit (ASIC), or other device) that may be used by host device 102 to execute appropriate instructions, such as software instructions provided in memory 196. Display 197 may be used to display captured and/or processed infrared images and/or other images, data, and information. Other components 198 may be used to implement any features of host device 102 as may be desired for various applications (e.g., clocks, temperature sensors, a visible light camera, or other components). In addition, a machine readable medium 193 may be provided for storing non-transitory instructions for loading into memory 196 and execution by processor 195.
 In various embodiments, infrared imaging module 100 and socket 104 may be implemented for mass production to facilitate high volume applications, such as for implementation in mobile telephones or other devices (e.g., requiring small form factors).
 In one embodiment, the combination of infrared imaging module 100 and socket 104 may exhibit overall dimensions of approximately 8.5 mm by 8.5 mm by 5.9 mm while infrared imaging module 100 is installed in socket 104.
 In these and other embodiments, processing module 160 may also be implemented with other components where appropriate, such as, volatile memory, non-volatile memory, and/or one or more interfaces (e.g., infrared detector interfaces, inter-integrated circuit (12C) interfaces, mobile industry processor interfaces (MIPI), joint test action group (JTAG) interfaces (e.g., IEEE 1149.1 standard test access port and boundary-scan architecture), and/or other interfaces).
 Further techniques for performing spatial row and column FPN correction processing are set forth in U.S. patent application No. Ser. 12/396,340 filed Mar. 2, 2009 which is incorporated herein by reference in its entirety.
 Following block 560, it is expected that any high spatial frequency content remaining in the blurred image frame may be generally attributed to spatially uncorrelated FPN. In this regard, following block 560, much of the other noise or actual desired scene based information has been removed or excluded from the blurred image frame due to: intentional blurring of the image frame (e.g., by motion or defocusing in blocks 520 through 545), application of row and column FPN terms (block 555), and contrast determination of (block 560).
 For example, FIG. 7 illustrates a flat field correction technique 700 in accordance with an embodiment of the disclosure.
 In FIG. 7, a NUC term may be determined for each pixel 710 of the blurred image frame using the values of its neighboring pixels 712 to 726. For each pixel 710, several gradients may be determined based on the absolute difference between the values of various adjacent pixels. For example, absolute value differences may be determined between: pixels 712 and 714 (a left to right diagonal gradient), pixels 716 and 718 (a top to bottom vertical gradient), pixels 720 and 722 (a right to left diagonal gradient), and pixels 724 and 726 (a left to right horizontal gradient).
 In one embodiment, the risk of introducing scene information into the NUC terms can be further reduced by applying some amount of temporal damping to the NUC term determination process. For example, a temporal damping factor A between 0 and 1 may be chosen such that the new NUC term (NUCNEW) stored is a weighted average of the old NUC term (NUCOLD) and the estimated updated NUC term (NUCUDDATE) In one embodiment, this can be expressed as NUCNEW=λNUCOLD (1-λ)(NUCOLD+NUCUPDATE).
 Systems and methods disclosed herein, in accordance with one or more embodiments, provide image processing algorithms for images captured by infrared imaging systems. For example, in one embodiment, the infrared images may be processed to reduce noise within the infrared images (e.g., improve image detail and/or image quality). For one or more embodiments, processing techniques may be applied to reduce noise within a row and/or a column of the infrared image.
 A significant portion of noise may be defined as row and column noise. This type of noise may be explained by non-linearities in a Read Out Integrated Circuit (ROIC). This type of noise, if not eliminated, may manifest as vertical and horizontal stripes in the final image and human observers are particularly sensitive to these types of image artifacts. Other systems relying on imagery from infrared sensors, such as, for example, automatic target trackers may also suffer from performance degradation, if row and column noise is present.
 Because of non-linear behavior of infrared detectors and read-out integrated circuit (ROIC) assemblies, even when a shutter operation or external black body calibration is performed, there may be residual row and column noise (e.g., the scene being imaged may not have the exact same temperature as the shutter). The amount of row and column noise may increase over time, after offset calibration, increasing asymptotically to some maximum value. In one aspect, this may be referred to as 1/f type noise.
 In any given frame, the row and column noise may be viewed as high frequency spatial noise. Conventionally, this type of noise may be reduced using filters in the spatial domain (e.g., local linear or non-linear low pass filters) or the frequency domain (e.g., low pass filters in Fourier or Wavelet space). However, these filters may have negative side effects, such as blurring of the image and potential loss of faint details.
 It should be appreciated by those skilled in the art that any reference to a column or a row may include a partial column or a partial row and that the terms "row" and "column" are interchangeable and not limiting. Thus, without departing from the scope of the invention, the term "row" may be used to describe a row or a column, and likewise, the term "column" may be used to describe a row or a column, depending upon the application.
 FIG. 12 shows a block diagram of a system 1200 (e.g., an infrared camera or other type of imaging system) for infrared image capturing and processing in accordance with an embodiment. The system 1200 comprises, in one implementation, a processing component 1210, a memory component 1220, an image capture component 1230, a control component 1240, and a display component 1250. Optionally, the system 1200 may include a sensing component 1260.
 The system 1200 may represent an infrared imaging device, such as an infrared camera, to capture and process images, such as video images of a scene 1270. The system 1200 may represent any type of infrared camera adapted to detect infrared radiation and provide representative data and information (e.g., infrared image data of a scene). For example, the system 1200 may represent an infrared camera that is directed to the near, middle, and/or far infrared spectrums. In another example, the infrared image data may comprise non-uniform data (e.g., real image data that is not from a shutter or black body) of the scene 1270, for processing, as set forth herein. The system 1200 may comprise a portable device and may be incorporated, e.g., into a vehicle (e.g., an automobile or other type of land-based vehicle, an aircraft, or a spacecraft) or a non-mobile installation requiring infrared images to be stored and/or displayed.
 In various embodiments, the processing component 1210 comprises a processor, such as one or more of a microprocessor, a single-core processor, a multi-core processor, a microcontroller, a logic device (e.g., a programmable logic device (PLD) configured to perform processing functions), a digital signal processing (DSP) device, etc. The processing component 1210 may be adapted to interface and communicate with components 1220, 1230, 1240, and 1250 to perform method and processing steps and/or operations, as described herein. The processing component 1210 may include a noise filtering module 1212 adapted to implement a noise reduction and/or removal algorithm (e.g., a noise filtering algorithm, such as discussed in reference to FIGS. 13A-13C). In one aspect, the processing component 1210 may be adapted to perform various other image processing algorithms including scaling the infrared image data, either as part of or separate from the noise filtering algorithm.
 It should be appreciated that noise filtering module 1212 may be integrated in software and/or hardware as part of the processing component 1210, with code (e.g., software or configuration data) for the noise filtering module 1212 stored, e.g., in the memory component 1220. Embodiments of the noise filtering algorithm, as disclosed herein, may be stored by a separate computer-readable medium (e.g., a memory, such as a hard drive, a compact disk, a digital video disk, or a flash memory) to be executed by a computer (e.g., a logic or processor-based system) to perform various methods and operations disclosed herein. In one aspect, the computer-readable medium may be portable and/or located separate from the system 1200, with the stored noise filtering algorithm provided to the system 1200 by coupling the computer-readable medium to the system 1200 and/or by the system 1200 downloading (e.g., via a wired link and/or a wireless link) the noise filtering algorithm from the computer-readable medium.
 The memory component 1200 comprises, in one embodiment, one or more memory devices adapted to store data and information, including infrared data and information. The memory device 1220 may comprise one or more various types of memory devices including volatile and non-volatile memory devices, such as RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically-Erasable Read-Only Memory), flash memory, etc. The processing component 1210 may be adapted to execute software stored in the memory component 1220 so as to perform method and process steps and/or operations described herein.
 The image capture component 1230 comprises, in one embodiment, one or more infrared sensors (e.g., any type of multi-pixel infrared detector, such as a focal plane array) for capturing infrared image data (e.g., still image data and/or video data) representative of an image, such as scene 1270. In one implementation, the infrared sensors of the image capture component 1230 provide for representing (e.g., converting) the captured image data as digital data (e.g., via an analog-to-digital converter included as part of the infrared sensor or separate from the infrared sensor as part of the system 1200). In one aspect, the infrared image data (e.g., infrared video data) may comprise non-uniform data (e.g., real image data) of an image, such as scene 1270. The processing component 1210 may be adapted to process the infrared image data (e.g., to provide processed image data), store the infrared image data in the memory component 1220, and/or retrieve stored infrared image data from the memory component 1220. For example, the processing component 1210 may be adapted to process infrared image data stored in the memory component 1220 to provide processed image data and information (e.g., captured and/or processed infrared image data).
 The control component 1240 comprises, in one embodiment, a user input and/or interface device, such as a rotatable knob (e.g., potentiometer), push buttons, slide bar, keyboard, etc., that is adapted to generate a user input control signal. The processing component 1210 may be adapted to sense control input signals from a user via the control component 1240 and respond to any sensed control input signals received therefrom. The processing component 1210 may be adapted to interpret such a control input signal as a value, as generally understood by one skilled in the art.
 In one embodiment, the control component 1240 may comprise a control unit (e.g., a wired or wireless handheld control unit) having push buttons adapted to interface with a user and receive user input control values. In one implementation, the push buttons of the control unit may be used to control various functions of the system 1200, such as autofocus, menu enable and selection, field of view, brightness, contrast, noise filtering, high pass filtering, low pass filtering, and/or various other features as understood by one skilled in the art. In another implementation, one or more of the push buttons may be used to provide input values (e.g., one or more noise filter values, adjustment parameters, characteristics, etc.) for a noise filter algorithm. For example, one or more push buttons may be used to adjust noise filtering characteristics of infrared images captured and/or processed by the system 1200.
 The display component 1250 comprises, in one embodiment, an image display device (e.g., a liquid crystal display (LCD)) or various other types of generally known video displays or monitors. The processing component 1210 may be adapted to display image data and information on the display component 1250. The processing component 1210 may be adapted to retrieve image data and information from the memory component 1220 and display any retrieved image data and information on the display component 1250. The display component 1250 may comprise display electronics, which may be utilized by the processing component 1210 to display image data and information (e.g., infrared images). The display component 1250 may be adapted to receive image data and information directly from the image capture component 1230 via the processing component 1210, or the image data and information may be transferred from the memory component 1220 via the processing component 1210.
 The optional sensing component 1260 comprises, in one embodiment, one or more sensors of various types, depending on the application or implementation requirements, as would be understood by one skilled in the art. The sensors of the optional sensing component 1260 provide data and/or information to at least the processing component 1210. In one aspect, the processing component 1210 may be adapted to communicate with the sensing component 1260 (e.g., by receiving sensor information from the sensing component 1260) and with the image capture component 1230 (e.g., by receiving data and information from the image capture component 1230 and providing and/or receiving command, control, and/or other information to and/or from one or more other components of the system 1200).
 In various implementations, the sensing component 1260 may provide information regarding environmental conditions, such as outside temperature, lighting conditions (e.g., day, night, dusk, and/or dawn), humidity level, specific weather conditions (e.g., sun, rain, and/or snow), distance (e.g., laser rangefinder), and/or whether a tunnel or other type of enclosure has been entered or exited. The sensing component 1260 may represent conventional sensors as generally known by one skilled in the art for monitoring various conditions (e.g., environmental conditions) that may have an effect (e.g., on the image appearance) on the data provided by the image capture component 1230.
 In some implementations, the optional sensing component 1260 (e.g., one or more of sensors) may comprise devices that relay information to the processing component 1210 via wired and/or wireless communication. For example, the optional sensing component 1260 may be adapted to receive information from a satellite, through a local broadcast (e.g., radio frequency (RF)) transmission, through a mobile or cellular network and/or through information beacons in an infrastructure (e.g., a transportation or highway information beacon infrastructure), or various other wired and/or wireless techniques.
 In various embodiments, components of the system 1200 may be combined and/or implemented or not, as desired or depending on the application or requirements, with the system 1200 representing various functional blocks of a related system. In one example, the processing component 1210 may be combined with the memory component 1220, the image capture component 1230, the display component 1250, and/or the optional sensing component 1260. In another example, the processing component 1210 may be combined with the image capture component 1230 with only certain functions of the processing component 1210 performed by circuitry (e.g., a processor, a microprocessor, a logic device, a microcontroller, etc.) within the image capture component 1230. Furthermore, various components of the system 1200 may be remote from each other (e.g., image capture component 1230 may comprise a remote sensor with processing component 1210, etc. representing a computer that may or may not be in communication with the image capture component 1230).
 In accordance with an embodiment of the invention, FIG. 13A shows a method 1320 for noise filtering an infrared image. In one implementation, this method 1320 relates to the reduction and/or removal of temporal, 1/f, and/or fixed spatial noise in infrared imaging devices, such as infrared imaging system 1200 of FIG. 12. The method 1320 is adapted to utilize the row and column based noise components of infrared image data in a noise filtering algorithm. In one aspect, the row and column based noise components may dominate the noise in imagery of infrared sensors (e.g., approximately 2/3 of the total noise may be spatial in a typical micro-bolometer based system).
 In one embodiment, the method 1320 of FIG. 13A comprises a high level block diagram of row and column noise filtering algorithms. In one aspect, the row and column noise filter algorithms may be optimized to use minimal hardware resources.
 Referring to FIG. 13A, the process flow of the method 1320 implements a recursive mode of operation, wherein the previous correction terms are applied before calculating row and column noise, which may allow for correction of lower spatial frequencies. In one aspect, the recursive approach is useful when row and column noise is spatially correlated. This is sometimes referred to as banding and, in the column noise case, may manifest as several neighboring columns being affected by a similar offset error. When several neighbors used in difference calculations are subject to similar error, the mean difference used to calculate the error may be skewed, and the error may only be partially corrected. By applying partial correction prior to calculating the error in the current frame, correction of the error may be recursively reduced until the error is minimized or eliminated. In the recursive case, if the HPF is not applied (block 1308), then natural gradients as part of the image may, after several iterations, be distorted when merged into the noise model. In one aspect, a natural horizontal gradient may appear as low spatially correlated column noise (e.g., severe banding). In another aspect, the HPF may prevent very low frequency scene information to interfere with the noise estimate and, therefore, limits the negative effects of recursive filtering.
 Referring to method 1320 of FIG. 13A, infrared image data (e.g., a raw video source, such as from the image capture component 1230 of FIG. 12) is received as input video data (block 1300). Next, column correction terms are applied to the input video data (block 1301), and row correction terms are applied to the input video data (block 1302). Next, video data (e.g., "cleaned" video data) is provided as output video data (1319) after column and row corrections are applied to the input video data. In one aspect, the term "cleaned" may refer to removing or reducing noise (blocks 1301, 1302) from the input video data via, e.g., one or more embodiments of the noise filter algorithm.
 Referring to the processing portion (e.g., recursive processing) of FIG. 13A, a HPF is applied (block 1308) to the output video data 1319 via data signal path 1319a. In one implementation, the high pass filtered data is separately provided to a column noise filter portion 1301a and a row noise filter portion 1302a.
 1. Apply previous column noise correction terms to a current frame as calculated in a previous frame (block 1301).
 2. High pass filter the row of the current frame by subtracting the result of a low pass filter (LPF) operation (block 1308), for example, as discussed in reference to FIGS. 14A-14C.
 3. For each pixel, calculate a difference between a center pixel and one or more (e.g., eight) nearest neighbors (block 1314). In one implementation, the nearest neighbors comprise one or more nearest horizontal neighbors. The nearest neighbors may include one or more vertical or other non-horizontal neighbors (e.g., not pure horizontal, i.e., on the same row), without departing from the scope of the invention.
 4. If the calculated difference is below a predefined threshold, add the calculated difference to a histogram of differences for the specific column (block 1309).
 5. At an end of the current frame, find a median difference by examining a cumulative histogram of differences (block 1310). In one aspect, for added robustness, only differences with some specified minimum number of occurrences may be used.
 6. Delay the current correction terms for one frame (block 1311), i.e., they are applied to the next frame.
 7. Add median difference (block 1312) to previous column correction terms to provide updated column correction terms (block 1313).
 8. Apply updated column noise correction terms in the next frame (block 1301).
 1. Apply previous row noise correction terms to a current frame as calculated in a previous frame (block 1302).
 2. High pass filter the column of the current frame by subtracting the result of a low pass filter (LPF) operation (block 1308), as discussed similarly above for column noise filter portion 1301a.
 3. For each pixel, calculate a difference between a center pixel and one or more (e.g., eight) nearest neighbors (block 1315). In one implementation, the nearest neighbors comprise one or more nearest vertical neighbors. The nearest neighbors may include one or more horizontal or other non-vertical neighbors (e.g., not pure vertical, i.e., on the same column), without departing from the scope of the invention.
 4. If the calculated difference is below a predefined threshold, add the calculated difference to a histogram of differences for the specific row (block 1307).
 5. At an end of the current row (e.g., line), find a median difference by examining a cumulative histogram of differences (block 1306). In one aspect, for added robustness only differences with some specified minimum number of occurrences may be used.
 6. Delay the current frame by a time period equivalent to the number of nearest vertical neighbors used, for example eight.
 7. Add median difference (block 1304) to row correction terms (block 1303) from previous frame (block 1305).
 8. Apply updated row noise correction terms in the current frame (block 1302). In one aspect, this may require a row buffer (e.g., as mentioned in 6).
 In one aspect, for all pixels (or at least a large subset of them) in each column, an identical offset term (or set of terms) may be applied for each associated column. This may prevent the filter from blurring spatially local details.
 Similarly, in one aspect, for all pixels (or at least a large subset of them) in each row respectively, an identical offset term (or set of terms) may be applied. This may inhibit the filter from blurring spatially local details.
 In one example, an estimate of the column offset terms may be calculated using only a subset of the rows (e.g., the first 32 rows). In this case, only a 32 row delay is needed to apply the column correction terms in the current frame. This may improve filter performance in removing high temporal frequency column noise. Alternatively, the filter may be designed with minimum delay, and the correction terms are only applied once a reasonable estimate can be calculated (e.g., using data from the 32 rows). In this case, only rows 33 and beyond may be optimally filtered.
 In one aspect, all samples may not be needed, and in such an instance, only every 2nd or 4th row, e.g., may be used for calculating the column noise. In another aspect, the same may apply when calculating row noise, and in such an instance, only data from every 4th column, e.g., may be used. It should be appreciated that various other iterations may be used by one skilled in the art without departing from the scope of the invention.
 In one aspect, the filter may operate in recursive mode in which the filtered data is filtered instead of the raw data being filtered. In another aspect, the mean difference between a pixel in one row and pixels in neighboring rows may be approximated in an efficient way if a recursive (IIR) filter is used to calculate an estimated running mean. For example, instead of taking the mean of neighbor differences (e.g., eight neighbor differences), the difference between a pixel and the mean of the neighbors may be calculated.
 In accordance with an embodiment of the invention, FIG. 13B shows an alternative method 1330 for noise filtering infrared image data. In reference to FIGS. 13A and 13B, one or more of the process steps and/or operations of method 1320 of FIG. 13A have changed order or have been altered or combined for the method 1320 of FIG. 13B. For example, the operation of calculating row and column neighbor differences (blocks 1314, 1315) may be removed or combined with other operations, such as generating histograms of row and column neighbor differences (blocks 1307, 1309). In another example, the delay operation (block 1305) may be performed after finding the median difference (block 1306). In various examples, it should be appreciated that similar process steps and/or operations have similar scope, as previously described in FIG. 13A, and therefore, the description will not be repeated.
 In still other alternate approaches to methods 1320 and 1330, embodiments may exclude the histograms and rely on mean calculated differences instead of median calculated differences. In one aspect, this may be slightly less robust but may allow for a simpler implementation of the column and row noise filters. For example, the mean of neighboring rows and columns, respectively, may be approximated by a running mean implemented as an infinite impulse response (IIR) filter. In the row noise case, the IIR filter implementation may reduce or even eliminate the need to buffer several rows of data for mean calculations.
 In still other alternate approaches to methods 1320 and 1330, new noise estimates may be calculated in each frame of the video data and only applied in the next frame (e.g., after noise estimates). In one aspect, this alternate approach may provide less performance but may be easier to implement. In another aspect, this alternate approach may be referred to as a non-recursive method, as understood by those skilled in the art.
 For example, in one embodiment, the method 1340 of FIG. 13C comprises a high level block diagram of row and column noise filtering algorithms. In one aspect, the row and column noise filter algorithms may be optimized to use minimal hardware resources. In reference to FIGS. 13A and 13B, similar process steps and/or operations may have similar scope, and therefore, the descriptions will not be repeated.
 Referring to FIG. 13C, the process flow of the method 1340 implements a non-recursive mode of operation. As shown, the method 1340 applies column offset correction term 1301 and row offset correction term 1302 to the uncorrected input video data from video source 1300 to produce, e.g., a corrected or cleaned output video signal 1319. In column noise filter portion 1301a, column offset correction terms 1313 are calculated based on the mean difference 1310 between pixel values in a specific column and one or more pixels belonging to neighboring columns 1314. In row noise filter portion 1302a, row offset correction terms 1303 are calculated based on the mean difference 1306 between pixel values in a specific row and one or more pixels belonging to neighboring rows 1315. In one aspect, the order (e.g., rows first or columns first) in which row or column offset correction terms 1303, 1313 are applied to the input video data from video source 1300 may be considered arbitrary. In another aspect, the row and column correction terms may not be fully known until the end of the video frame, and therefore, if the input video data from the video source 1300 is not delayed, the row and column correction terms 1303, 1313 may not be applied to the input video data from which they where calculated.
 In one aspect of the invention, the column and row noise filter algorithm may operate continuously on image data provided by an infrared imaging sensor (e.g., image capture component 1230 of FIG. 12). Unlike conventional methods that may require a uniform scene (e.g., as provided by a shutter or external calibrated black body) to estimate the spatial noise, the column and row noise filter algorithms, as set forth in one or more embodiments, may operate on real-time scene data. In one aspect, an assumption may be made that, for some small neighborhood around location [x, y], neighboring infrared sensor elements should provide similar values since they are imaging parts of the scene in close proximity. If the infrared sensor reading from a particular infrared sensor element differs from a neighbor, then this could be the result of spatial noise. However, in some instances, this may not be true for each and every sensor element in a particular row or column (e.g., due to local gradients that are a natural part of the scene), but on average, a row or column may have values that are close to the values of the neighboring rows and columns.
 For one or more embodiments, by first taking out one or more low spatial frequencies (e.g., using a high pass filter (HPF)), the scene contribution may be minimized to leave differences that correlate highly with actual row and column spatial noise. In one aspect, by using an edge preserving filter, such as a Median filter or a Bilateral filter, one or more embodiments may minimize artifacts due to strong edges in the image.
 In accordance with one or more embodiments of the invention, FIGS. 14A to 14C show a graphical implementation (e.g., digital counts versus data columns) of filtering an infrared image. FIG. 14A shows a graphical illustration (e.g., graph 1400) of typical values, as an example, from a row of sensor elements when imaging a scene. FIG. 14B shows a graphical illustration (e.g., graph 1410) of a result of a low pass filtering (LPF) of the image data values from FIG. 14A. FIG. 14C shows a graphical illustration (e.g., graph 1420) of subtracting the low pass filter (LPF) output in FIG. 14B from the original image data in FIG. 14A, which results in a high pass filter (HPF) profile with low and mid frequency components removed from the scene of the original image data in FIG. 14A. Thus, FIG. 14A-140 illustrate a HPF technique, which may be used for one or more embodiments (e.g., as with methods 1320 and/or 1330).
 In one aspect of the invention, a final estimate of column and/or row noise may be referred to as an average or median estimate of all of the measured differences. Because noise characteristics of an infrared sensor are often generally known, then one or more thresholds may be applied to the noise estimates.
 For example, if a difference of 60 digital counts is measured, but it is known that the noise typically is less than 10 digital counts, then this measurement may be ignored.
 In accordance with one or more embodiments of the invention, FIG. 15 shows a graphical illustration 1500 (e.g., digital counts versus data columns) of a row of sensor data 1501 (e.g., a row of pixel data for a plurality of pixels in a row) with column 5 data 1502 and data for eight nearest neighbors (e.g., nearest pixel neighbors, 4 columns 1510 to the left of column 5 data 1502 and 4 columns 1511 to the right of column 5 data 1502). In one aspect, referring to FIG. 4, the row of sensor data 1501 is part of a row of sensor data for an image or scene captured by a multi-pixel infrared sensor or detector (e.g., image capture component 1230 of FIG. 12). In one aspect, column 5 data 1502 is a column of data to be corrected. For this row of sensor data 1501, the difference between column 5 data 1502 and a mean 1503 of its neighbor columns (1510, 1511) is indicated by an arrow 1504. Therefore, noise estimates may be obtained and accounted for based on neighboring data.
 In accordance with one or more embodiments of the invention, FIGS. 16A to 16C show an exemplary implementation of column and row noise filtering an infrared image (e.g., an image frame from infrared video data). FIG. 16A shows an infrared image 1600 with column noise estimated from a scene with severe row and column noise present and a corresponding graph 1602 of column correction terms. FIG. 16B shows an infrared image 1610, with column noise removed and spatial row noise still present, with row correction terms estimated from the scene in FIG. 16A and a corresponding graph 1612 of row correction terms. FIG. 16C shows an infrared image 1620 of the scene in FIG. 16A as a cleaned infrared image with row and column noise removed (e.g., column and row correction terms of FIGS. 16A-16B applied).
 In one embodiment, FIG. 16A shows an infrared video frame (i.e., infrared image 1600) with severe row and column noise. Column noise correction coefficients are calculated as described herein to produce, e.g., 639 correction terms, i.e., one correction term per column. The graph 1602 shows the column correction terms. These offset correction terms are subtracted from the infrared video frame 1600 of FIG. 16A to produce the infrared image 1610 in FIG. 16B. As shown in FIG. 16B, the row noise is still present. Row noise correction coefficients are calculated as described herein to produce, e.g., 639 row terms, i.e., one correction term per row. The graph 1612 shows the row offset correction terms, which are subtracted from the infrared image 1610 in FIG. 16B to produce the cleaned infrared image 1620 in FIG. 16C with significantly reduced or removed row and column noise.
 In various embodiments, it should be understood that both row and column filtering is not required. For example, either column noise filtering 1301a or row noise filtering 1302a may be performed in methods 1320, 1330 or 1340.
 It should be appreciated that any reference to a column or a row may include a partial column or a partial row and that the terms "row" and "column" are interchangeable and not limiting. For example, without departing from the scope of the invention, the term "row" may be used to describe a row or a column, and likewise, the term "column" may be used to describe a row or a column, depending upon the application.
 In various aspects, column and row noise may be estimated by looking at a real scene (e.g., not a shutter or a black body), in accordance with embodiments of the noise filtering algorithms, as disclosed herein. The column and row noise may be estimated by measuring the median or mean difference between sensor readings from elements in a specific row (and/or column) and sensor readings from adjacent rows (and/or columns).
 Optionally, a high pass filter may be applied to the image data prior to measuring the differences, which may reduce or at least minimize a risk of distorting gradients that are part of the scene and/or introducing artifacts. In one aspect, only sensor readings that differ by less than a configurable threshold may be used in the mean or median estimation. Optionally, a histogram may be used to effectively estimate the median. Optionally, only histogram bins exceeding a minimum count may be used when finding the median estimate from the histogram. Optionally, a recursive IIR filter may be used to estimate the difference between a pixel and its neighbors, which may reduce or at least minimize the need to store image data for processing, e.g., the row noise portion (e.g., if image data is read out row wise from the sensor). In one implementation, the current mean column value Ci,j for column i at row j may be estimated using the following recursive filter algorithm.
 In this equation α is the damping factor and may be set to for example 0.2 in which case the estimate for the running mean of a specific column i at row j will be a weighted sum of the estimated running mean for column i-1 at row j and the current pixel value at row j and column i. The estimated difference between values of row j and the values of neighboring rows (ΔRi) can now be approximated by taking the difference of each value Ci,j and the running recursive mean of the neighbors above row i ( Ci-1,j). Estimating the mean difference this way is not as accurate as taking the true mean difference since only rows above are used but it requires that only one row of running means are stored as compared to several rows of actual pixel values be stored.
 In one embodiment, referring to FIG. 13A, the process flow of method 1320 may implement a recursive mode of operation, wherein the previous column and row correction terms are applied before calculating row and column noise, which allows for correction of lower spatial frequencies when the image is high pass filtered prior to estimating the noise.
 Generally, during processing, a recursive filter re-uses at least a portion of the output data as input data. The feedback input of the recursive filter may be referred to as an infinite impulse response (IIR), which may be characterized, e.g., by exponentially growing output data, exponentially decaying output data, or sinusoidal output data. In some implementations, a recursive filter may not have an infinite impulse response. As such, e.g., some implementations of a moving average filter function as recursive filters but with a finite impulse response (FIR).
 In various embodiments, an infrared imaging system may be implemented with an infrared sensor array that is fixably positioned to substantially de-align rows and columns of infrared sensors while a thermal image frame is captured of a scene. For example, in some embodiments, the infrared sensor array may be fixably positioned in a rotated orientation relative to the scene (e.g., rotated relative to the housing and/or rotated about an optical axis of the infrared imaging system). De-alignment of the infrared sensor array may prevent the rows and columns of infrared sensors from being substantially parallel with one or more substantially horizontal and/or substantially vertical features of the scene. As a result, such features of the scene may be imaged across several different rows and columns of the infrared sensors and may not be generally aligned with rows and columns of pixels of the captured thermal image frame.
 Such de-aligned orientations of the infrared sensor array may result in fewer row and column noise artifacts exhibited in captured thermal image frames of scenes including substantially horizontal and/or substantially vertical features. Moreover, such de-aligned orientations may prevent the imaged features from aligning with row and column noise and/or image artifacts that may result from noise reduction techniques applied to such row and column noise.
 In some embodiments, an infrared imaging system may be expected to be used in one or more particular physical orientations. For example, for a handheld infrared imaging system, an expected physical orientation may be a comfortable position in which the imaging system is usually held by a user. In another embodiment, for a mounted infrared imaging system, an expected physical orientation may be a position in which the infrared imaging system is usually mounted. In these and similar embodiments, while the infrared imaging system is positioned in an expected physical orientation, rows and columns of the infrared sensor array may be de-aligned from horizontal and vertical features of the scene as discussed. In some embodiments, the expected physical orientation may be a physical orientation in which the housing or other components of the imaging system are generally parallel to a first reference plane (e.g., a plane generally parallel to the ground) and the infrared sensor array may be generally parallel to and facing a second reference plane (e.g., a plane corresponding to a view of the scene as perceived by the infrared sensor array). Other orientations are also contemplated.
 FIG. 17 illustrates an infrared imaging system 1700 with a de-aligned infrared sensor array 1730 installed in a housing 1710 in accordance with an embodiment of the disclosure. FIG. 18 illustrates infrared sensor array 1730 relative to a scene 1750 to be imaged in accordance with an embodiment of the disclosure. In this regard, FIG. 17 provides a view of imaging system 1700 from the perspective of scene 1750.
 As shown in FIG. 17, infrared sensor array 1730 includes a plurality of infrared sensors 1722 implemented in a plurality of rows 1732 and columns 1734. Although a 10 by 10 array of infrared sensors 1722 is illustrated in FIG. 17, and desired array size may be used. Rows 1732 and columns 1734 may be substantially de-aligned with various features of scene 1750. For example, as shown in FIG. 18, a substantially horizontal feature 1782 may be substantially parallel to an X axis, and a substantially vertical feature 1784 may be substantially parallel to a Y axis. As shown in FIGS. 17 and 18, rows 1732 and columns 1734 may be de-aligned with respect to the X and Y axes (e.g., by an angle A).
 In some embodiments, infrared sensor array 1730 may be de-aligned relative to one or more surfaces of housing 1710. For example, in the particular embodiment illustrated in FIG. 17, housing 1710 includes surfaces 1712 and 1714 substantially parallel to the X axis, and surfaces 1716 and 1718 substantially parallel to the Y axis. Infrared sensor array 1730 may be fixably positioned within housing 1710 to substantially de-align rows 1732 and columns 1734 of infrared sensors 1722 (e.g., by angle A) such that rows 1732 and columns 1734 are not parallel to the X and Y axes, or any of surfaces 1712, 1714, 1716, and 1718 as shown in FIG. 17. As a result, rows 1732 and columns 1734 of infrared sensors 1722 will also be de-aligned with horizontal feature 1782 and vertical feature 1784 (e.g., by angle A as shown in FIG. 18).
 In the embodiments shown in FIGS. 17 and 18, an expected physical orientation of imaging system 1700 may correspond to surfaces 1712 and 1714 of housing 1710 being positioned substantially parallel to an X-Z plane (e.g., corresponding to the X and Z axes), and surfaces 1716 and 1718 of housing 1710 being positioned substantially parallel to a Y-Z plane (e.g., corresponding to the Y and Z axes). Although housing 1710 is illustrated with a generally square cross section in FIG. 17, any desired shape may be used. In this regard, straight (e.g., substantially planar) external surfaces of housing 1710 may be used in some embodiments, but other internal and external surfaces are also contemplated. Accordingly, the generally square cross section of housing 1710 in FIG. 17 is provided only to illustrate one example of an expected physical orientation of imaging system 1700 when in use. Accordingly, in other expected physical orientations, some surfaces or no surfaces of imaging system 1700 may be parallel to the X-Z or Y-Z planes.
 In some embodiments, infrared sensor array 1730 may be de-aligned based on a rotational offset. For example, infrared sensor array 1730 may include an optical axis 1740 along which infrared radiation is received. While mounted within housing 1710, infrared sensor array 1730 may be rotationally offset (e.g., by angle A) about optical axis 1740. Accordingly, while infrared imaging system 1700 is positioned in an expected physical orientation, infrared sensor array 1730 may be rotationally offset within an X-Y plane (e.g., corresponding to the X and Y axes) relative to housing 1710, scene 1750, and/or other aspects.
 In some embodiments, imaging system 1700 may include one or more optical components 1720 substantially aligned with optical axis 1740. In one embodiment, image capture component 1730 may be disposed within housing 1710 and behind optical components 1720 such that electromagnetic radiation from scene 1750 passes through optical components 1720 before being received by image capture component 1710.
 In some embodiments, various portions of infrared imaging system 1700 may be implemented by components previously described herein. For example, infrared imaging system 1700 may include any components of infrared imaging module 100, host device 102, system 1200, and/or other components as may be desired in particular implementations.
 In one embodiment, infrared imaging system 1700 may be implemented by infrared imaging module 100. For example, infrared sensors 1722 and infrared sensor array 1730 may be implemented by infrared sensors 132 of infrared sensor assembly 128, optical component 1720 may be implemented by optical element 180, and housing 1710 may be implemented by housing 120, socket 104, and/or host device 102. In such an embodiment, infrared imaging system 1700 may be implemented with one or more processing devices such as, for example, processing module 160, processor 195, and/or other devices to perform various operations described herein. Also in such an embodiment, infrared imaging system 1700 may be implemented with one or more memories such as, for example, memory 196 and/or other memories. Other embodiments using various components of imaging module 100 are also contemplated.
 In another embodiment, infrared imaging system 1700 may be implemented by system 1200. For example, infrared sensors 1722 and infrared sensor array 1730 may be implemented by image capture component 1230. In such an embodiment, infrared imaging system 1700 may be implemented with one or more processing devices such as, for example, processing component 1210 and/or other devices to perform various operations described herein. Also in such an embodiment, infrared imaging system 1700 may be implemented with one or more memories such as, for example, memory component 1220 and/or other memories. Other embodiments using various components of system 1200 are also contemplated.
 FIG. 19 illustrates a flow diagram of operations to obtain a thermal image frame using de-aligned infrared sensor array 1730 in accordance with an embodiment of the disclosure. At block 1910, infrared imaging system 1700 is provided (e.g., assembled and/or otherwise manufactured). Block 1910 may include various operations including, for example: providing infrared sensor array 1730, providing housing 1710; inserting infrared sensor array 1730 into housing 1710; positioning infrared sensor array 1730 in a substantially de-aligned position (e.g., by rotating infrared sensor array 1730 about optical axis 1740, rotating infrared sensor array 1730 relative to housing 1710, and/or other positioning techniques); fixing infrared sensor array 1730 in the substantially de-aligned position (e.g., by securing infrared sensor array 1730 directly to housing 1710 and/or to one or more other structures using appropriate mechanisms, adhesives, and/or other techniques); installing optical components 1720; installing any other components of infrared imaging system 1700; and/or other operations as appropriate.
 At block 1920, a particular scene 1750 to be imaged is selected. In one embodiment, such selection may be performed by a user of infrared imaging system 1700. In another embodiment, such selection may be performed by infrared imaging system 1700 itself (e.g., through appropriate selection processes performed by one or more processing devices).
 At block 1930, infrared imaging system 1700 is positioned relative to scene 1750. In one embodiment, such positioning may be performed by a user of infrared imaging system 1700. In another embodiment, such positioning may be performed by infrared imaging system 1700 itself (e.g., through operation of appropriate actuators and/or other mechanisms).
 At block 1940, infrared imaging system 1700 captures a thermal image frame of scene 1750. While the thermal image frame is captured in block 1940, rows 1732 and columns 1734 of infrared sensors 1722 of infrared sensor array 1730 are substantially de-aligned from substantially horizontal features (e.g., substantially horizontal feature 1782) and substantially vertical features (e.g., substantially vertical feature 1784) of scene 1750 as discussed.
 For example, FIG. 20 illustrates a thermal image frame 2000 captured during block 1940 of FIG. 19 in accordance with an embodiment of the disclosure. Thermal image frame 2000 includes a plurality of pixels 2022 arranged in a plurality of rows 2032 and columns 2034. In some embodiments, pixels 2022 may correspond to infrared sensors 1722 of infrared sensor array 1730. In other embodiments, greater or fewer pixels 2022 may be used relative to infrared sensors 1722 (e.g., using appropriate pixel interpolation techniques and/or other processing). In the particular example shown in FIG. 20, the size of pixels 2022 has been enlarged and the number of pixels 2022 have been reduced to more clearly illustrate the de-alignment of pixel rows 2032 and pixel columns 2034 relative to features 1782 and 1784. As shown in FIG. 20, features 1782 and 1784 are substantially horizontal and substantially vertical buildings in this example, however any desired features may be imaged in various embodiments.
 At block 1950, row and column noise correction processing is performed on thermal image frame 2000 in accordance with the various techniques described herein. At block 1960, scene based NUC processing is performed on thermal image frame 2000 in accordance with the various techniques described herein.
 At block 1970, further processing may be performed on thermal image frame 2000 as may be desired in particular implementations. For example, thermal image frame 2000 may be processed to provide a cropped thermal image frame 2100 as shown in FIG. 21 in accordance with an embodiment of the disclosure. In particular, cropped thermal image frame 2100 has been cropped with a border 2110 that is substantially parallel with features 1782 and 1784 of scene 1750. As a result, cropped thermal image frame 2100 may generally appear to have an orientation corresponding to the view of scene 1750 from infrared imaging system 1700. In the particular example shown in FIG. 21, the size of pixels 2022 has been enlarged and the number of pixels 2022 have been reduced for purposes of illustration similar to FIG. 20.
 In view of the present disclosure, it will be appreciated that the use of de-aligned infrared sensor array 1730 permits thermal image frame 2000 and cropped thermal image frame 2100 to exhibit improvements over images captured with conventionally aligned sensors. For example, because thermal image frame 2100 was captured by de-aligned rows 1732 and columns 1734 of infrared sensor array 1730, thermal image frame 2100 and cropped thermal image frame 2100 may exhibit fewer row and column noise artifacts associated with features 1782 and 1784. In addition, any possible artifacts resulting from noise reduction operations (e.g., row and column noise correction processing of block 1950 and/or other noise reduction operations) will not be aligned with features 1782 and 1784 in thermal image frame 2100 and cropped thermal image frame 2100.

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