January 19, 2017

Lapped MDCT-based image conditioning with optical aberrations correction, color conversion, edge emphasis and noise reduction

by Andrey Filippov

Fig.1. Image comparison of the different processing stages output

Results of the processing of the color image

Previous blog post “Lens aberration correction with the lapped MDCT” described our experiments with the lapped MDCT[1] for optical aberration corrections of a single color channel and separation of the asymmetrical kernel into a small asymmetrical part for direct convolution and a larger symmetrical one to be applied in the frequency domain of the MDCT. We supplemented this processing chain with additional steps of the image conditioning to evaluate the overall quality of the of the results and feasibility of the MDCT approach for processing in the camera FPGA.

Image comparator in Fig.1 allows to see the difference between the images generated from the results of the several stages of the processing. It makes possible to compare any two of the image layers by either sliding the image separator or by just clicking on the image – that alternates right/left images. Zoom is controlled by the scroll wheel (click on the zoom indicator fits image), pan – by dragging.

Original image was acquired with Elphel model 393 camera with 5 Mpix MT9P006 image sensor and Sunex DSL227 fisheye lens, saved in jp4 format as a raw Bayer data at 98% compression quality. Calibration was performed with the Java program using calibration pattern visible in the image itself. The program is designed to work with the low-distortion lenses so fisheye was a stretch and the calibration kernels near the edges are just replicated from the ones closer to the center, so aberration correction is only partial in those areas.

First two layers differ just by added annotations, they both show output of a simple bilinear demosaic processing, same as generated by the camera when running in JPEG mode. Next layers show different stages of the processing, details are provided later in this blog post.

January 7, 2017

Lens aberration correction with the lapped MDCT

by Andrey Filippov

Modern small-pixel image sensors exceed resolution of the lenses, so it is the optics of the camera, not the raw sensor “megapixels” that define how sharp are the images, especially in the off-center areas. Multi-sensor camera systems that depend on the tiled images do not have any center areas, so overall system resolution may be as low as that of is its worst part.

Fig. 1. Lateral chromatic aberration and Bayer mosaic: a) monochrome (green) PSF, b) composite color PSF, c) Bayer mosaic of the sensor (direction of aberration shown), d) distorted mosaic matching chromatic aberration in b).

Fig. 1. Lateral chromatic aberration and Bayer mosaic: a) monochrome (green) PSF, b) composite color PSF, c) Bayer mosaic of the sensor, d) distorted mosaic for the chromatic aberration of b).

De-mosaic processing and chromatic aberrations

Our current cameras role is to preserve the raw sensor data while providing some moderate compression, all the image correction is applied during post-processing. Handling the lens aberration has to be done before color conversion (or de-mosaicing). When converting Bayer data to color images most cameras start with the calculation of the “missing” colors in the RG/GB pattern using 3×3 or 5×5 kernels, this procedure relies on the specific arrangement of the color filters.

Each of the red and blue pixels has 4 green ones at the same distance (pixel pitch) and 4 of the opposite (R for B and B for R) color at the equidistant diagonal locations. Fig.1. shows how lateral chromatic aberration disturbs these relations.

Fig.1a is the point-spread function (PSF) of the green channel of the sensor. The resolution of the PSF measurement is twice higher than the pixel pitch, so the lens is not that bad – horizontal distance between the 2 greens in Fig.1c corresponds to 4 pixels of Fig.1a. It is also clearly visible that the PSF is elongated and the radial resolution in this part of the image is better than the tangential one (lens center is left-down).

Fig.1b shows superposition of the 3 color channels: blue center is shifted up-and-right by approximately 2 PSF pixels (so one actual pixel period of the sensor) and the red one – half-pixel left-and-down from the green center. So the point light of a star, centered around some green pixel will not just spread uniformly to the two “R”s and two “B”s shown connected with lines in Fig.1c, but the other ones and in different order. Fig.1d illustrates the effective positions of the sensor pixels that match the lens aberration.


December 17, 2016

DCT type IV implementation

by Andrey Filippov

As we finished with the basic camera functionality and tested the first Eyesis4π built with the new 10393 system boards (it is smaller, requires less power and, is faster) we are moving forward with the in-camera image processing. We plan to combine our current camera calibration methods that require off-line post processing and the real-time image correction using the camera own FPGA resources. This project development will require switching between the actual FPGA coding and the software implementation of the same algorithms before going to the next step – software is still easier to design. The first part was in FPGA realm – it was to implement the fundamental image processing block that we already know we’ll be using and see how much of the resources it needs.

DCT type IV as a building block for in-camera image processing

We consider a small (8×8 pixel) DCT-IV to be a universal block for conditioning of the raw acquired images. Such operations as lens optical aberrations correction, color conversion (de-mosaic) in the presence of the lateral chromatic aberration, image rectification (de-warping) are easier to perform in the frequency domain using convolution-multiplication property and other algorithms.

In post-processing we use DFT (Discrete Fourier Transform) over rather large (64×64 to 512×512) tiles, but that would be too much for the in-camera processing. First is the tile size – for good lenses we do not need that large convolution kernels. Additionally we plan to combine several processing steps into one (based on our off-line post-processing experience) and so we do not need to sub-sample images – in our current software we double resolution of the raw images at the beginning and scale back the final result to reduce image degradation caused by re-sampling.

The second area where we plan to reduce computations is the replacement of the DFT with the DCT that is designed to be fed with the pure real data and so requires less arithmetic operations than DFT that processes complex input values.

Why “type IV” of the DCT?

Fig.1. Signal flow graph for DCT-IV

Fig.1. Signal flow graph for DCT-IV

We already have DCT type II implemented for the JPEG/JP4 compression, and we still needed another one. Type IV is used in audio compression because it can be converted to a modified discrete cosine transform (MDCT) – a procedure when multiple overlapped windows are processed one at a time and the results are seamlessly combined without any block artifacts that are familiar for the JPEG with low settings of the compression quality. We too need lapped transform to process large images with relatively small (much smaller than the image itself) convolution kernels, and DCT-IV is a perfect fit. 8-point DCT-IV allows to implement transformation of 16-point segments with 8-point overlap in a reversible manner – the inverse transformation of 8-point data may be converted to 16-point overlapping segments, and being added together these segments result in the original data.

September 19, 2016

NC393 development progress and the future plans

by Andrey Filippov

Since we started to deliver first NC393 series cameras in May we were working on the cameras software – original version was rather limited. While it was capable of serving images/video over the network and recording them on the internal m.2 SSD, it did not have the advanced image acquisition control (through the GUI and programmatically) that was standard for the earlier NC353 series. Now the core functionality is operational and in a month we plan to have the remaining parts (inter-camera synchronization, working with multiple sensors per-port with 10359 multiplexer, GPS+IMU logging) online too. FPGA code is already ported, but it needs to be tested and a fair amount of troubleshooting, identifying the problems and weeding out the bugs is still left to be done.

Fig 1. Four camvc instances for four channels of NC393 camera

Fig 1. Four camvc instances for the four channels of NC393 camera

Users of earlier Elphel cameras can easily recognize familiar camvc web interface – Fig. 1 shows a screenshot of the four instances of this interface controlling 4 sensors of NC393 camera in “H” configuration.

July 11, 2016

I will not have to learn SystemVerilog

by Andrey Filippov

Or at least larger (verification) part of it – interfaces, packages and a few other synthesizable features are very useful to reduce size of Verilog code and make it easier to maintain. We now are able to run production target system Python code with Cocotb simulation over BSD sockets.

Client-server simulation of NC393 with Cocotb

Client-server simulation of NC393 with Cocotb

Previous workflow

Before switching to Cocotb our FPGA-related workflow involved:

  1. Creating RTL design code
  2. Writing Verilog tests
  3. Running simulations
  4. Synthesizing and creating bitfile
  5. Re-writing test code to run on the target system in Python
  6. Developing kernel drivers to support the FPGA functionality
  7. Developing applications that access FPGA functionality through the kernel drivers

Of course the steps are not that linear, there are hundreds of loops between steps 1 and 3 (editing RTL source after finding errors at step 3), almost as many from 5 to 1 (when the problems reveal themselves during hardware testing) but few are noticed only at step 6 or 7. Steps 2, 5, 6+7 involve a gross violation of DRY principle, especially the first two. The last steps sufficiently differ from step 5 as their purpose is different – while Python tests are made to reveal the potential problems including infrequent conditions, drivers only use a subset of functionality and try to “hide” problems – perform recovering actions to maintain operation of the device after abnormal condition occurs.

May 22, 2016

Tutorial 02: Eclipse-based FPGA development environment for Elphel cameras

by Andrey Filippov

Elphel cameras offer unique capabilities – they are high performance systems out of the box and have all the firmware and FPGA code distributed under GNU General Public Licenses making it possible for users to modify any part of the code. The project does not use any “black boxes” or encrypted modules, so it is simulated with the free software tools and user has access to every net in the design. We are trying to do our best to make this ‘hackability’ not just a theoretical possibility, but a practical one.

Current camera FPGA project contains over 400 files under version control and almost 100K lines of HDL (Verilog) code, there are also constraints files, tool configurations, so we need to provide means for convenient navigation and modification of the project by the users.

We are starting a series of tutorials to facilitate acquaintance with this project, and here is the first one that shows how to install and configure the software. This tutorial is made with a fresh Kubuntu 16.04 LTS distribution installed on a virtual machine – this flavor of GNU/Linux we use ourselves and so it is easier for us to help others in the case of problems, but it should be also easy to install it on other GNU/Linux systems.

Later we plan to show how to navigate code and view/modify tool parameters with VDT plugin, run simulation and implementation tools. Next will be a “Hello world” module added to the camera code base, then some simple module that accesses the video memory.

Video resolution is 1600×900 pixels, so full screen view is recommended.

Download links for: video and captions.

Running this software does not require to have an actual camera, so it may help our potential users to evaluate software capabilities and see if it matches their requirements before purchasing an actual hardware. We will also be able to provide remote access to the cameras in our office for experimenting with them.

May 10, 2016

3D Print Your Camera Freedom

by Andrey Filippov

Two weeks ago we were making photos of our first production NC393 camera to post an announcement of the new product availability. We got all the mechanical parts and most of the electronic boards (14MPix version will be available shortly) and put them together. Nice looking camera, powered by a high performance SoC (dual ARM plus FPGA), packaged in a lightweight aluminum extrusion body, providing different options for various environments – indoors, outdoors, on board of the UAV or even in the open space with no air (cooling is important when you run most of the FPGA resources at full speed). Tons of potential possibilities, but the finished camera did not seem too exciting – there are so many similar looking devices available.

NC393 camera, front view

NC393 camera, back panel view. Includes DC power input (12-36V and 20-75V options), GigE, microSD card (bootable), microUSB(type B) connector for a system console with reset and boot source selection, USB/eSATA combo connector, microUSB(type A) and 2.5mm 4-contact barrel connector for external synchronization I/O

NC393 assembled boards: 10393(system board), 10385 (power supply board), 10389(interface board), 10338e (sensor board) and 103891 - synchronization adapter board, view from 10389. m.2 2242 SSD shown, bracket for the 2260 format provided. 10389 internal connectors include inter-camera synchronization and two of 3.3VDC+5.0VDC+I2C+USB ones.

NC393 assembled boards: 10393(system board), 10385 (power supply board), 10389(interface board), 10338e (sensor board) and 103891 - synchronization adapter board, view from 10385

10393 system board attached to the heat frame, view from the heat frame. There is a large aluminum heat spreader attached to the other side of the frame with thermal conductive epoxy that provides heat transfer from the CPU without the use of any spring load. Other heat dissipating components use heat pads.

10393 system board attached to the heat frame, view from the 10393 board

10393 system board, view from the processor side

An obvious reason for our dissatisfaction is that the single-sensor camera uses just one of four available sensor ports. Of course it is possible to use more of the freed FPGA resources for a single image processing, but it is not what you can use out of the box. Many of our users buy camera components and arrange them in their custom setup themselves – that does not have a single-sensor limitation and it matches our goals – make it easy to develop a custom system, or sculpture the camera to meet your ideas as stated on our web site. We would like to open the cameras to those who do not have capabilities of advanced mechanical design and manufacturing or just want to try new camera ideas immediately after receiving the product.

March 30, 2016

Synchronizing Verilog, Python and C

by Andrey Filippov

Elphel NC393 as all the previous camera models relies on the intimate cooperation of the FPGA programmed in Verilog HDL and the software that runs on a general purpose CPU. Just as the FPGA manufacturers increase the speed and density of their devices, so do the Elphel cameras. FPGA code consists of the hundreds of files, tens of thousand lines of code and is constantly modified during the lifetime of the product both by us and by our users to accommodate the cameras for their applications. In most cases, if it is not just a bug fix or minor improvement of the previously implemented functionality, the software (and multiple layers of it) needs to be aware of the changes. This is both the power and the challenge of such hybrid systems, and the synchronization of the changes is an important issue.


March 18, 2016

Free FPGA: Reimplement the primitives models

by Andrey Filippov

We added the AHCI SATA controller Verilog code to the rest of the camera FPGA project, together they now use 84% of the Zynq slices. Building the FPGA bitstream file requires proprietary tools, but all the simulation can be done with just the Free Software – Icarus Verilog and GTKWave. Unfortunately it is not possible to distribute a complete set of the files needed – our code instantiates a few FPGA primitives (hard-wired modules of the FPGA) that have proprietary license.

Please help us to free the FPGA devices for developers by re-implementing the primitives as Verilog modules under GNU GPLv3+ license – in that case we’ll be able to distribute a complete self-sufficient project. The models do not need to provide accurate timing – in many cases (like in ours) just the functional simulation is quite sufficient (combined with the vendor static timing analysis). Many modules are documented in Xilinx user guides, and you may run both the original and replacement models through the simulation tests in parallel, making sure the outputs produce the same signals. It is possible that such designs can be used as student projects when studying Verilog.

March 12, 2016

AHCI/SATA stack under GNU GPL

by Andrey Filippov

Implementation includes AHCI SATA host adapter in Verilog under GNU GPLv3+ and a software driver for GNU/Linux running on Xilinx Zynq. Complete project is simulated with Icarus Verilog, no encrypted modules are required.

This concludes the last major FPGA development step in our race against finished camera parts and boards already arriving to Elphel facility before the NC393 can be shipped to our customers.

Fig. 1. AHCI Host Adapter block diagram

Fig. 1. AHCI Host Adapter block diagram

Why did we need SATA?

Elphel cameras started as network cameras – devices attached to and controlled over the Ethernet, the previous generations used 100Mbps connection (limited by the SoC hardware), and NC393 uses GigE. But this bandwidth is still not sufficient as many camera applications require high image quality (compared to “raw”) without compression artifacts that are always present (even if not noticeable by the human viewer) with the video codecs. Recording video/images to some storage media is definitely an option and we used it in the older camera too, but the SoC IDE controller limited the recording speed to just 16MB/s. It was about twice more than the 100Mb/s network, but still was a bottleneck for the system in many cases. The NC393 can generate 12 times the pixel rate (4 simultaneous channels instead of a single one, each running 3 times faster) of the NC353 so we need 200MB/s recording speed to keep the same compression quality at the increased maximal frame rate, higher recording rate that the modern SSD are capable of is very desirable.

Fig.2. SATA routing

Fig.2. SATA routing: a) Camera records data to the internal SSD; b) Host computer connects directly to the internal SSD; c) Camera records to the external mass storage device

The most universal ways to attach mass storage device to the camera would be USB, SATA and PCIe. USB-2 is too slow, USB-3 is not available in Xilinx Zynq that we use. So what remains are SATA and PCIe. Both interfaces are possible to implement in Zynq, but PCIe (being faster as it uses multiple lanes) is good for the internal storage while SATA (in the form of eSATA) can be used to connect external storage devices too. We may consider adding PCIe capability to boost recording speed, but for initial implementation the SATA seems to be more universal, especially when using a trick we tested in Eyesis series of cameras for fast unloading of the recorded data.

Routing SATA in the camera

It is a solution similar to USB On-The-Go (similar term for SATA is used for unrelated devices), where the same connector is used to interface a smartphone to the host PC (PC is a host, a smartphone – a device) and to connect a keyboard or other device when a phone becomes a host. In contrast to the USB cables the eSATA ones always had identical connectors on both ends so nothing prevented to physically link two computers or two external drives together. As eSATA does not carry power it is safe to do, but nothing will work – two computers will not talk to each other and the storage devices will not be able to copy data between them. One of the reasons is that two signal pairs in SATA cable are uni-directional – pair A is output for the host and input for device, pair B – the opposite.

Camera uses Vitesse (now Microsemi) VSC3304 crosspoint switch (Eyesis uses larger VSC3312) that has a very useful feature – it has reversible I/O ports, so the same physical pins can be configured as inputs or outputs, making it possible to use a single eSATA connector in both host and device mode. Additionally VSC3304 allows to change the output signal level (eSATA requires higher swing than the internal SATA) and perform analog signal correction on both inputs and outputs facilitating maintaining signal integrity between attached SATA devices.

Aren’t SATA implementations for Xilinx Zynq already available?

Yes and no. When starting the NC393 development I contacted Ashwin Mendon who already had SATA-2 working on Xilinx Virtex. The code is available on OpenCores under GNU GPL license. There is an article published by IEEE . The article turned out to be very useful for our work, but the code itself had to be mostly re-written – it was still for different hardware and were not able to simulate the core as it depends on Xilinx proprietary encrypted primitives – a feature not compatible with the free software simulators we use.

Other implementations we could find (including complete commercial solution for Xilinx Zynq) have licenses not compatible with the GNU GPLv3+, and as the FPGA code is “compiled” to a single “binary” (bitstream file) it is not possible to mix free and proprietary code in the same design.

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