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.

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.

October 25, 2012

Heptaclops camera and the 393

by Andrey Filippov

“Temporary diversion” that lasted for three years

Last years we were working on the multi-sensor cameras and optical parts of the cameras. It all started as a temporary diversion from the development of the model 373 cameras that we planned to use instead of our current model 353 cameras based on the discontinued Axis CPU. The problem with the 373 design was that while the prototype was assembled and successfully tested (together with two new add-on boards) I did not like the bandwidth between the FPGA and the CPU – even as I used as many connection channels between them as possible. So while the Texas Instruments DaVinci processor was a significant upgrade to the camera CPU power, the camera design did not seem to me as being able to stay current for the next 3-5 years and being able to accommodate new emerging (not yet available) sensors with increased resolution and frame rate. This is why we decided to put that design on hold being ready to start the production if our the number of our stored Axis CPU would fall dangerously low. Meanwhile wait for the better CPU/FPGA integration options to appear and focus on the development of the other parts of the system that are really important.

Now that wait for the processor is nearly over and it seems to be just in time – we still have enough stock to be able to provide NC353 cameras until the replacement will be ready. I’ll get to this later in the post, and first tell where did we get during these 3 years.

September 24, 2012

Building and Calibrating Eyesis4π

by Andrey Filippov

This is a long overdue post describing our work on the Eyesis4π camera, an attempt to catch up with the developments of the last half of a year. The design of the camera started a year before that and I described the planned changes from the previous model in Eyesis4πi post. Oleg wrote about the assembly progress and since that post we did not post any updates.

October 31, 2011

Subpixel Registration and Distortion Measurement

by Andrey Filippov


While working on the second generation of the Eyesis panoramic cameras, we decided to try go from capturing the series of the individual panoramic images to the 3d reconstruction. There are multiple successful implementations of such process, we just plan to achieve higher precision of capturing the 3d worlds using Elphel ability to design and build the hardware specific for such purpose. While most projects are designed to work with the standard off-the-shelf cameras, we are working on building the cameras together with the devices and methods for these cameras calibration.  In order to be able to precisely determine the 3-d locations of the features registered with the cameras we plan first go as far as possible to precisely map each pixel of each sub-camera (of the composite camera) image to the ray in space. That would require at least two distinctive steps: