Patent Publication Number: US-11657523-B2

Title: Microlens amplitude masks for flying pixel removal in time-of-flight imaging

Description:
This application is a 371 application of International Application No. PCT/IB2022/052448, which has an International Filing Date of Mar. 17, 2022, which claims the benefit of U.S. Provisional Patent Application No. 63/162,336, filed on Mar. 17, 2021, both of which are incorporated herein by reference in their entirety. 
    
    
     FEDERALLY SPONSORED RESEARCH 
     This invention was made with government support under Grant No. IIS-2047359 awarded by the National Science Foundation (NSF). The United States Government has certain rights in the invention. 
    
    
     BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The embodiments of the present invention generally relate to image processing, and more particularly, toward techniques for three-dimensional (3D) image processing and depth determination. 
     Discussion of the Related Art 
     At present, typical time-of-flight (ToF) depth capture cameras collect light incident to a lens, focus the incident light onto a sensor (i.e., along the sensor plane), and measure an output at each pixel of a pixel array sensor (e.g., a complementary metal-oxide-semiconductor (“CMOS”) type sensor). In some instances, one or more individual pixels receive a mixed-light signal. Depending on the subject matter of the image, the mixed-light may originate from multiple object surfaces at varying depths. 
     ToF imaging can be further categorized into direct and indirect techniques. Direct ToF devices such as light detection and ranging (“LiDAR”) send out pulses of light, scanning over a scene and directly measuring their round-trip time using photodiodes or photon detectors. While accurate and long-ranged, these systems can produce only a few spatial measurements at a time, resulting in sparse depth maps. Furthermore, their specialized detectors are orders of magnitude more expensive than conventional CMOS sensors. 
     Amplitude modulated continuous wave (“AMCW”) ToF imaging is a type of indirect ToF. AMCW devices instead flood the whole scene with periodically modulated light and infer depth from phase differences between captures (i.e., using a plurality of correlation images at varying phase offsets). These captures can be acquired with a standard CMOS sensor, making AMCW ToF cameras an affordable solution for dense depth measurement. 
     In current ToF imaging applications, both direct ToF (e.g., LiDAR) and indirect ToF (e.g., AMCW), the resultant estimated depth for a given pixel is incorrect when mixed-light is received. A so-called “flying pixel” has an estimated depth that is between the objects of varying depths. As neighboring pixels also included mixed-light, neighboring pixels cannot be reliably used to disambiguate the flying pixel artifact. 
       FIG.  1    illustrates a correlation imager system  100  according to the related art. As illustrated in  FIG.  1   , ToF camera  110  illuminates (depicted as illumination  111 ) a target  120  with continuously modulated light. The light is reflected by target  120  (depicted as reflected signal  112 ), which results in an accrued depth-dependent phase shift  113 . The light is collected on camera sensor  114 , converted to an electrical signal  115 , and correlated with an on-board reference signal  116  to produce a time-of-flight correlation measurement  117  and one or more correlation images. 
       FIG.  2    illustrates the use of multiple correlation images to calculate depth according to the related art. As illustrated in  FIG.  2   , by collecting multiple (e.g., four) correlation images  211 A,  211 B,  211 C,  211 D with varying phase offsets (e.g., 0, π, π/2, 3π/2, respectively), the phase of the reflected light can be determined. Here, the correlation values C at each of the varying phase offsets can be used to extract the measured signal true phase ϕ according to: 
                     ϕ   =       arctan   ⁡   (         C   ⁡   (   π   )     -     C   ⁡   (     π   /   2     )           C   ⁡   (   0   )     -     C   ⁡   (     3   ⁢     π   /   2       )         )     +     2   ⁢   π   ⁢   n         ,           Eq   .           (   1   )                 
where 2πn is a phase ambiguity for certain depths. For each pixel, the phase ϕ is calculated. Subsequently, a phase map  220  of the correlation images can be converted into a depth map  230 . For each pixel, depth z is calculated according to:
 
 z=ϕc /4πω,   Eq. (2)
 
where c is the speed of light and w is a modulation frequency of the amplitude modulated light that is used for illumination (depicted as illumination  111  in  FIG.  1   ).
 
     However, the related art techniques are subject to various limitations and drawbacks. For example, indirect ToF methods are still subject to fundamental limitations of the sensing process including noise from ambient light, photon shot, phase wrapping, multipath interference (MPI), and flying pixels. 
       FIG.  3    illustrates the reflection of a mixed-light signal according to the related art. As illustrated in  FIG.  3   , foreground object  221  and background object  222  are illuminated (depicted as illumination  111 ). ToF camera lens  218  receives reflected light from both objects through aperture  219 , and focuses the light to produce foreground signal  231  and background signal  232  on pixel  220  of sensor  114 . 
     Mixed light including foreground signal  231  and background signal  232  are used to calculate the depth of the target object (e.g., either foreground object  221  or background object  222 ). However, the mixed light produces a mixed depth measurement, and the calculated depth does not accurately reflect the depth of the target object and a flying pixel  240  is produced. 
     Flying pixels, such as flying pixel  240 , frequently occur around or near depth edges, where light paths from both an object and its background or foreground are integrated over the aperture. 
     One common solution to reduce flying pixel count is to narrow the camera aperture. However, use of a narrow aperture also reduces overall light throughput and increases the system&#39;s susceptibility to noise. While a narrower aperture could reduce the effects of flying pixels, it is not light efficient, and leads to high noise susceptibility in the measurements. 
     Unfortunately, such a masking approach (i.e., reducing aperture size) significantly lowers the signal-to-noise ratio (“SNR”). Thus, there exists a strict SNR verses flying pixel tradeoff for typical ToF depth cameras. 
     Accordingly, the inventors have developed mask-ToF learning microlens masks for flying pixel correction in ToF imaging to overcome the limitations and drawbacks of the related art devices. 
     SUMMARY 
     Accordingly, the present invention is directed to microlens amplitude masks for flying pixel removal in time-of-flight imaging that substantially obviates one or more problems due to limitations and disadvantages of the related art. 
     Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. 
     As discussed above, flying pixels are pervasive artifacts that occur at object boundaries, where background and foreground light mix to produce erroneous measurements that can negatively impact downstream 3D vision tasks, such as depth determination. The embodiments of the present invention generate a microlens-level occlusion mask pattern which modulates the selection of foreground and background light on a per-pixel basis. 
     When configured in an end-to-end fashion with a depth refinement network, the embodiments of the present invention are able to effectively decode these modulated measurements to produce high fidelity depth reconstructions with significantly reduced flying pixel counts. 
     To achieve these and other advantages and in accordance with the purpose of the present invention, as embodied and broadly described, the microlens amplitude masks for flying pixel removal in time-of-flight imaging includes systems, devices, methods, and instructions for image depth determination, including receiving an image, adding noise to the image, determining a set of correlation images, each correlation image having a varying phase offset, for each pixel of the image, generating a masked pixel by applying a mask array, and for each masked pixel, determining the depth of the masked pixel to generate a depth map for the image on a per pixel basis. 
     In another aspect, the microlens amplitude masks for flying pixel removal in time-of-flight imaging includes systems, devices, methods, and instructions for image depth determination, including a time-of-flight system for image depth determination, the system a lens configured to receive incident light, and a light sensor having a plurality of pixels, the light sensor configured to receive the incident light through a plurality of masks, each pixel corresponding to a respective mask that selectively blocks incident light paths to provide a differentiable apertures for neighboring pixels. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. 
         FIG.  1    illustrates a correlation imager system according to the related art. 
         FIG.  2    illustrates the use of multiple correlation images to calculate depth according to the related art. 
         FIG.  3    illustrates the reflection of a mixed-light signal according to the related art. 
         FIG.  4    illustrates a camera system having a microlens mask according to an example embodiment of the present invention. 
         FIGS.  5 A and  5 B  illustrate the data used for generation of a mask pattern according to an example embodiment of the present invention. 
         FIG.  6    illustrates the generation of a masked pixel according to an example embodiment of the present invention. 
         FIG.  7    illustrates the generation of a decoded depth construction according to an example embodiment of the present invention. 
         FIG.  8    illustrates the updating of a mask pattern according to an example embodiment of the present invention. 
         FIG.  9    illustrates a computer-implemented method for depth determination according to an example embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Reference will now be made in detail to the embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Wherever possible, like reference numbers will be used for like elements. 
     Flying pixels are pervasive artifacts in ToF imaging which occur at object discontinuities, where both foreground and background light signal is integrated over the camera aperture. The light mixes at a sensor pixel to produce erroneous depth estimates, which then adversely affect downstream 3D vision tasks, such as depth determination. The embodiments of the present invention introduce a custom-shaped sub-aperture for each sensor pixel. For example, the embodiments of the present invention generate a microlens-level occlusion mask which effectively generates a custom-shaped sub-aperture for each sensor pixel. By customizing the aperture for each sensor pixel, the effects of flying pixels are significantly reduced. 
       FIG.  4    illustrates a camera system  400  having a microlens mask  411  according to an example embodiment of the present invention. 
     Microlens mask  411 , selected from a plurality microlens mask patterns  410 , is disposed between sensor (e.g., CMOS sensor pixel  420 ) and microlens  430 . The aperture of microlens mask  411  is configured to selectively block incident light paths to enable a custom aperture for each pixel. This modulates the selection of foreground and background light mixtures on a per-pixel basis and further encodes scene geometric information directly into the ToF measurements. Thus, microlens mask  411  provides spatially varying susceptibility to noise and flying pixels, and is used to de-noise and reduce the occurrence of flying pixels. In addition, use of microlens mask  411 , with its learned mask pattern (as described below), further enables measurements from neighboring pixels with different effective apertures to provide additional data to accurately identify and rectify flying pixels. 
     For example, a mask  411  may be photolithographically disposed on each pixel of sensor  420  during fabrication of the sensor. A custom optical relay system was used to validate the mask pattern. In another example, the mask  411  can be fabricated directly on each pixel of sensor  420 . Although camera system  400  depicts a microlens  430 , microlens mask  411 , and pixel of sensor  420 , the embodiments are not so limited. A variety of lens sizes and types can be used, a mask array having a plurality of masks  411  can be used, and a variety of sensor types can be used. 
       FIGS.  5 A and  5 B  illustrate the data used for generation of a mask pattern according to an example embodiment of the present invention. 
     As illustrated in  FIG.  5 A , the generation of a mask is driven by an image having a set of light field data  510  that includes scene view data from multiple viewing angles. Using as input a set of light field data  510 , a set of correlation values  521  (as a function of cos(φ+ψ), where φ is the phase and ψ is the phase offset) produce a set of correlation images  511 A,  511 B,  511 C,  511 D that are determined for each sub-aperture view at varying phase offsets (e.g., 0, π, π/2, 3π/2, respectively). Correlation values can include weights to encode depth data of depth map  530 . Typically, depth data of depth map  530  is determined using ToF measurements. 
     Simulated noise  522  is added to light field data  510  or the set of correlation images  511 A,  511 B,  511 C,  511 D at varying phase offsets (e.g., 0, π, π/2, 3π/2, respectively). For example, simulated noise  522  can include noise according to a Poisson distribution or a Skellam distribution that approximates Gaussian noise. The introduction of noise can be to simulate system and/or environmental perturbations. 
     As illustrated in  FIG.  5 B , to determine the microlens mask, ToF data of correlation image  511  with sub-aperture views  541 - 549  are used. Sub-aperture views  541 - 549  correspond to a subsection of a respective correlation image, such as a subsection or pixel group  540 . In addition, each of sub-aperture views  541 - 549  corresponds to respective viewing angle data contained in light field data  510 . 
     As there are no available datasets, the set of light field data  510  of correlation image  511  with depth map  530  are used to determine ToF amplitude measurements. In some embodiments, the time of flight measurements are decoded or otherwise extracted from the set of light field data  510  to determine initial depth estimate for depth map  530 . 
       FIG.  6    illustrates the generation of a masked pixel according to an example embodiment of the present invention. 
     By multiplication of a set of sub-aperture pixels  640  (e.g., including sub-aperture pixels  641 - 649 ) by a mask array  650  (e.g., including a set of micro-lens masks  651 - 659 ) and summing the results on a per pixel basis, a masked pixel  660  is produced. Here, sub-aperture pixels  640  are weighted according to a mask array  650 . As discussed above, simulated noise can be added, and the weighted sub-aperture pixels are combined with the simulated noise to produce an initial depth estimate on a per pixel basis. 
       FIG.  7    illustrates the generation of a decoded depth construction according to an example embodiment of the present invention. 
     For a given masked correlation image, each generated masked pixel  660  (e.g., generated using is masking process as illustrated in  FIG.  6   ) is processed by a convolution refinement network  770  to output a decoded and refined depth reconstruction map  780 . Initial depth estimates are input as masked pixels  660  to convolution refinement network  770  that decodes the spatially varying pixel measurements to produce refined (e.g., more accurate, more granular, etc.) depth estimates as refined depth reconstruction map  780 . 
     In some embodiments, an estimated depth map can be generated from multiple (e.g., four) masked correlation images. The depth can be estimated using Eq. (1) and Eq. (2), or alternatively, other depth estimation techniques can be used, such as the discrete Fourier transform. 
     Convolution refinement network  770  is a residual encoder-decoder model, implemented using a memory and a graphical processing unit (“GPU”) or other processor, that utilizes an initial depth estimate and mask information as input to refined depth reconstruction map  780 . For example, refined depth reconstruction map  780  can be calculated according to
 
{circumflex over (D)}*= R ( P ( C ),  M )=max(0, {circumflex over (D)}+{circumflex over (D)} R ),   Eq. (3)
 
where D{circumflex over ( )}* is the refined depth map, R is the convolution refinement network, P(C) is the initial depth estimate, M is the mask, D{circumflex over ( )} is the initial depth estimate, and D{circumflex over ( )} R  is the refined residual depth which when added to D{circumflex over ( )} serves to correct the now spatially multiplexed effects of noise and flying pixels.
 
     Eq. (3) in contrast to Eq. (1) and Eq. (2) introduces the use of an initial depth calculation. In addition, convolution refinement network  770  does not generate depth from phase, and the processing and computational needs of convolution refinement network  770  are substantially reduced as compared to a conventional deep reconstruction network. As a result, convolution refinement network  770  quickly determines high level depth and mask features, as well as determines other image information where raw phase data might significantly differ from a training set. The sequential depth estimation and refinement approach also enables calibration procedures implemented by the sensor manufacturers. Real depth data can be supplied to convolution refinement network  770  without having to retrain and learn calibration offsets. 
     Thus, the encoder-decoder model of convolution refinement network  770  is configured to aggregate the spatial information and utilize mask structural cues to produce refined depth estimates. The errors between initial depth estimates and refined depth estimates can be used to improve mask patterns. 
       FIG.  8    illustrates the updating of a mask pattern according to an example embodiment of the present invention. 
     At convolution refinement network  770 , errors in depth calculations (e.g., between the initial depth and refined depth) are calculated. Calculating the errors with respect to the light field depth, the errors can be used to improve convolution refinement network  770  and mask array  650  (e.g., as illustrated in  FIG.  6   ). In this way, starting at an initial mask, the embodiments can simultaneously determine an encoding or otherwise update a mask pattern and decoding network weights. The updated mask pattern can be applied on (e.g., photolithographically) each pixel of a sensor (e.g., sensor  420 ) during fabrication of the sensor. 
     With a global aperture of the related art, as illustrated in  FIG.  3   , all pixels are equally susceptible to flying pixels, and if one sensor pixel returns a flying pixel, likely so will its neighboring sensor pixels. Returning to the embodiments of the present invention, the addition of spatially variable susceptibility via a microlens mask, as illustrated in  FIG.  4   , for example, means that neighboring pixels are no longer equally susceptible to noise and/or flying pixels. A sensor pixel with a wide effective aperture can be trusted with regards to noise statistics, but is likely to return flying pixels if near an object boundary. Contrastingly, a neighboring pixel with a narrow aperture will likely produce noisier measurements, but be less affected by depth discontinuities. By aggregating information in pixel neighborhoods, wide aperture pixels can be used to de-noise local measurements, and narrow aperture pixels can be used to reduce the occurrence of flying-pixels. 
       FIG.  9    illustrates a computer-implemented method  900  for depth determination according to an example embodiment of the present invention. The computer-implemented method can be implemented using one or more memory devices (e.g., a non-transitory memory), one or more processing devices (e.g., a CPU, GPU, etc.), and/or one or more communication channels to transmit one or more instructions. 
     At  910 , method  900  receives an image (e.g., an image containing a set of light field data  510  as illustrated in  FIG.  5   ). The image may include one or more objects, one or more surfaces, and is captured by a ToF camera. 
     Next, at  920 , method  900  adds simulated noise (e.g., noise  522  as illustrated in  FIG.  5   ) to the image. Simulated noise is added to the image. For example, simulated noise can include noise according to a Poisson distribution or a Skellam distribution that approximates Gaussian noise. 
     Subsequently, for the image, method  900  generates a set of correlation images, each correlation image having a varying phase offset (e.g., correlation images  511 A,  511 B,  511 C,  511 D as illustrated in  FIG.  5   ), at  930 . 
     At  940 , for each pixel of the image, method  900  generates a masked pixel by applying a mask array. As discussed in connection with  FIG.  6   , by multiplying a set of sub-aperture pixels (such as sub-aperture pixels  641 - 649 ) with a mask array (such as mask array  650 , including a set of micro-lens masks  651 - 659 ) and summing the results on a per pixel basis, a masked pixel (such as masked pixel  660 ) is produced. Here, sub-aperture pixels  640  are weighted according to a mask array  650 . The weighted sub-aperture pixels are combined with the simulated noise to produce an initial depth estimate on a per pixel basis. 
     Lastly, for each masked pixel, method  900  determines the depth of the masked pixel to generate a depth map for the image on a per pixel basis. Here, the respective depths of masked pixels can be determined using a convolution refinement network  770  (such as convolution refinement network  770 ). Alternatively, or additionally, other known depth determination techniques may be used. 
     In implementation, it was demonstrated that a pinhole aperture produces an extremely noisy reconstruction; an open aperture produces blurred edges with a plethora of flying pixels; and the mask pattern provides substantially improved depth determination with acceptable SNR and substantially reduced flying pixels. For real scene captures, the mask pattern achieves a 30% reduction in flying pixels as compared to an identical light throughput using a global aperture mask. In addition, the results generalize to scenes of varying geometry and surface material. Moreover, the results were achieved without re-training or fine-tuning the convolution refinement network. 
     The embodiments of the invention can be readily applied to numerous applications. Some non-exhaustive examples include cameras for mobile phones or tablets, autonomous vehicles, collision avoidance, delivery robotics, cartography including topography and other 3D maps, gaming, augmented reality (“AR”), virtual reality (“VR”), facial identification, and others. 
     It will be apparent to those skilled in the art that various modifications and variations can be made in the microlens amplitude masks for flying pixel removal in time-of-flight imaging of the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.