Why Prism Cameras Are Transforming Medical and Life Sciences

Medical and life science imaging applications are doing ever more challenging applications. This is partly due to evolving technology and what that enables. And of course also to applications ingenuity.

These applications continue to push the need for:

Improved image quality
Better color accuracy
Higher sensitivity
More reliable data for AI and diagnostics

Key takeaway:

Prism-based cameras overcome many of the limitations of traditional Bayer color cameras by capturing true color information without interpolation.

Brief review of Bayer color interpolation

A Bayer filter “color” camera uses a monochrome sensor fitted with a Bayer filter, outputting color image derived through interpolation. Interpolation is a form of approximation – some precision is lost.

Please chase that Wikipedia link above if you want the full tutorial on the Bayer filter approach. It was and remains a very clever and effective technique for color imaging that works effectively for the vast majority of color applications. But not all…

Per the Wikipedia article, downsides of the Bayer approach can include artefacts like Moiré patterns, false color, and/or zippering.

If you are pushing the envelope of precision high-resolution color imaging, to innovate a new application or to stay ahead of your competition, you may want to consider prism-based color cameras, for better color accuracy and improved image quality.

Representative applications that may benefit:

Digital pathology
Microscopy
Cell analysis
Histology
Pharmaceutical inspection
Medical device inspection

How prism cameras work

Incoming light passes through a dichroic prism, which splits the light into separate color channels directed onto individual monochrome sensors. The optical path through the lens and up until reaching the prism is identical, to insure pixel alignment.

Benefits:

True RGB at every pixel (no interpolation)
Superior color accuracy
Better spatial resolution
Improved repeatability

Already intrigued? Prefer to just speak about it? Call us at 978-474-0044 for expert assistance. Or briefly describe your application in the form linked below and request a callback:

What color prism cameras are available?

JAI Apex prism-based color cameras are available with interfaces ranging over USB3, GigE, 5GigE, Camera Link, 10GigE, and CoaXpress.

JAI Apex prism-based color camera with USB interface – Courtesy JAI

Why Prism Technology Excels in Microscopy

Better color accuracy
Differentiation of stains and dyes
Improved visualization of cellular structures
Improved quantitative analysis

Digital pathology requires precise color images, aiding in the differentiation of stains and dyes; and to visualize structures.

Courtesy JAI.

Fluorescence microscopy helps make specific proteins and compounds observable. Prism cameras ensure even subtle changes are identifiable.

Courtesy JAI.

Surgical Imaging and NIR Fluorescence

Now let’s take it up a level. While not restricted to surgical navigation, either robotic or conventional, it’s clear that real-time surgical navigation is mission critical. Tissue differentiation is essential.

Consider the benefits of not just precision color, as discussed above, but the added benefit of NIR, from the same optical path and at the same position. A four-sensor prism-based camera can do exactly that. So the surgeon (whether human or robotic) may compare a color image to an NIR image in the same orientation and moment, to achieve tissue differentiation.

By virtue of using both VIS and NIR portions of the spectrum, such a camera is considered multi-spectral.

Consider the JAI Fusion family of prism cameras, utilizing GigE and 10GigE interfaces to deliver high frame rate multispectral images with RGB and NIR.

Rear and front views of one member of the JAI Fusion series – Courtesy JAI

Multispectral applications

Below we see that a 4 sensor camera (R, G, B, and NIR) permits a single camera to deliver pixel level information across multiple spectra, without requiring pixel-matching in software. This example happens to be in fruit sorting.

Another food sorting example:

In the potato sorting application below, visible light may be helpful for some aspects of inspection, but NIR identifies defects that VIS doesn’t see. A prism-based multispectral camera adds clear value.

1stVision’s Knowledge Base has TechBriefs on camera and sensor selection, lighting, filters, optics, and so forth. And we’re always happy to advise by phone 978-474-0044 or via our inquiry form.

1st Vision’s sales engineers have over 100 years of combined experience to assist in your camera and components selection. With a large portfolio of cameras, lenses, cables, NIC cards and industrial computers, we can provide a full vision solution!

About you: We want to hear from you! We’ve built our brand on our know-how and like to educate the marketplace on imaging technology topics… What would you like to hear about?… Drop a line to info@1stvision.com with what topics you’d like to know more about.

#prismcamera

#medicalimagingcamera

#JAI

October 27, 2026October 28, 2026

Teledyne DALSA Falcon4-CLHS-M8200 scientific and high speed cameras

Note: Below we introduce both the Scientific and High-speed versions of the Falcon4 67MP cameras. They are both based on the same sensor, and offer many of the same features. But one is optimized for speed (90 fps), and the other for scientific applications (65 fps) – still impressively fast.

The Teledyne Falcon4 67.1 MP monochrome area scan cameras designed for demanding applications. One version is optimized for scientific and quantitative imaging applications; the other for high-speed. The camera features 8192 × 8192 resolution with 2.5 µm pixels. Square large-format APS-C sensor and Camera Link HS (CLHS) interface.

Image courtesy Teledyne DALSA.

Let’s compare these sister cameras side-by-side:

Comparing the scientific vs. the high-speed versions of the camera – Courtesy Teledyne DALSA

A few notes about the side-by-side comparison above:

Teledyne DALSA developed the high-speed version first, the Falcon4-CLHS M8200. Per our TechBrief Machine Vision Sensor Attributes Explained, the high-speed version of the camera is already a high performer.
But certain applications, especially in the life-sciences, demand even higher performance in terms of dynamic range, quantum efficiency, and the lowest possible noise. So Teledyne DALSA spun off the scientific version of the camera, with alternative firmware, added antireflective sensor coating, and reduced the clock rate, now offering the Falcon4-CLHS M8200 Scientific model.

Scientific imaging isn’t only about getting a good-looking image For quantitative imaging, the camera is being used as a measurement instrument, not simply to identify an object. The Falcon4-CLHS M8200-Scientific provides pixel-level factory calibration, in addition to its low read noise, higher quantum efficiency and wide dynamic range. These characteristics are intended to support applications where image consistency and measurement accuracy are critical.

Falcon4-CLHS M8200

Falcon4-CLHS M8200 Scientific

Why a square 8K x 8K sensor?

The square sensor format maximizes usable imaging area for microscopy applications. With a 2.5um square pixel, that’s how they got to a 67MP sensor in a compact area. Yes, that puts demands on optics, but a lenses with good MTF performance. Lens quality and specifications vary – we can help you find a great lens for these cameras and your application requirements.

Applications areas include:

Whole-slide imaging, genomics, and spatial omics, fluorescence microscopy, high-content screening, automated cell analysis, imaging cytometry, and light-sheet microscopy.

The autofluorescence of a Scots pine sample. This is a composite image (red and blue) from a cross-section of a six-week-old Scots pine seedling, demonstrating autofluorescence in different wavelengths. *Courtesy Teledyne DALSA.*

The camera’s combination of low noise, high sensitivity, wide dynamic range, and factory-calibrated image quality also makes it well suited for precision metrology, materials analysis, semiconductor inspection, and other applications where accuracy, throughput, and image consistency are critical.

#DALSAFalconFA-HMM8205

#scientificimaging

#highspeedcamera

October 20, 2026October 27, 2026

5 Signs Your Machine Vision Application Needs SWIR Imaging

Most machine vision systems begin with visible-light cameras, but many inspection challenges remain unsolved because the information being sought simply isn’t visible in the VIS spectrum.

Many are intimidated by SWIR, worried it’s some exotic sensing technology. While it’s different in that it’s a form of vision that’s in a different part of the spectrum than VIS, we all enjoy radio and bluetooth, and we’re not natively equipped to sense those parts of the spectrum either!

Below we present SWIR as a practical problem-solving technology. And we’ve created a new area on the 1stVision website dedicated to SWIR imaging.

Sign #1: You Need to Detect Moisture or Water Content

Drone mounted SWIR sensor identifies moisture content in crops – could be used to determine irrigation needs – Courtesy Tamron

Applications:

Fruit and vegetable inspection
Agricultural monitoring
Crop stress analysis
Food quality grading
Moisture detection

VIS image left; SWIR image right reveals moisture content – Courtesy Allied Vision

Key takeaway: Water absorbs SWIR wavelengths, creating contrast that visible-light systems often cannot provide.

The economic payoff for fruit sorting with SWIR is to detect early bruising. That way bruised fruit isn’t shipped for display and customer rejection and poor quality – instead lightly bruised fruit can still be quickly sauced or juiced, while heavily bruised fruit may be rejected early to reduce shipping costs and preserve reputation for quality.

Sign #2: Different Materials Look the Same in Visible Light

Applications:

Plastic sorting
Recycling systems
Material identification
Foreign material detection
Food processing

Key takeaway: Materials that appear identical in visible light often separate very clearly in SWIR.

VIS left; SWIR right: differentiate materials with SWIR – Courtesy Allied Vision

Consider recycling and automated sorting applications: If the materials aren’t visible to humans, then neither humans nor VIS spectrum machine vision are candidates. But SWIR applications can distinguish among different types of plastic, for example, making recycling profitable or at least less costly. Also relevant for food safety applications.

Not sure if SWIR is right for your application?

Our machine vision specialists can review your application and recommend the right camera, lens, lighting, and filters. There is no cost for this consultation.

Sign #3: You Need to See Through Silicon

*VIS image (left) only finds the large crack; SWIR image (right) also finds smaller upper crack – Courtesy JAI*

Applications:

Semiconductor inspection
Wafer inspection
Die inspection
Solar manufacturing

Key takeaway: Silicon becomes transparent at SWIR wavelengths, allowing inspection of features hidden from visible cameras.

Economics: Semiconductor and solar panel production is an expensive undertaking, and it’s essential to detect any flaws as early as possible, to reject any materials that aren’t worthy of further processing. Improving yields reduces costs and raises quality, providing competitive advantage.

Sign #4: You Need to Inspect Through Plastic Packaging

*SWIR clearly reveals seal quality better than VIS – Courtesy JAI*

Left: VIS image of same scene Right in SWIR – Courtesy QDI Systems

Pharmaceutical packaging
Medical devices
Bottle inspection
Seal inspection
Fill-level verification

Key takeaway: SWIR can provide visibility through packaging materials that are difficult to inspect using conventional imaging.

Especially many plastics are opaque in VIS yet transparent in SWIR. The benefits are clear – pun initially not intended but in hindsight it works.

Need to inspect products through plastic packaging?

Explore our SWIR camera solutions or contact us to discuss your inspection requirements.

View SWIR cameras.

Sign #5: Glare, Dust, Smoke, or Haze Is Limiting Performance

Note how SWIR “sees through” smoke – Courtesy Sony

Applications:

Outdoor inspection
Transportation systems
Industrial process monitoring
Harsh manufacturing environments

Key takeaway: SWIR wavelengths can often provide improved contrast and image quality in challenging environments.

How SWIR Technology Has Changed in the Last Five Years

Many engineers who looked at SWIR way back before 2020 may think of it as expensive and niche. Back then that was a fair characterization. It was still cost-effective in defined niches when the ROI made sense, but it’s fair to say it was niche.

But automatic transmission cars were once niche. As were smartphones. And streaming video. Technologies evolve.

Recent SWIR developments – two in particular:

One: Sony SenSWIR

Commercialized in just the past few years, Sony’s SenSWIR IMX992 and IMX 993 sensors continue to use InGaAs. But with innovative bonding technologies Sony has improved quantum efficiency, permitting dense pixels, smaller sensors, and the avoidance of cooling.

That’s enabled small-format cost-effective cameras such as Allied Vision’s Alvium SWIR models. Note that SenSWIR isn’t “just” SWIR (many previous SWIR cameras could only work in the SWIR portion of the spectrum), but VIS+SWIR:

Higher resolutions
Smaller pixels
Faster frame rates
More compact camera designs
Easier machine vision integration

Two: Nextgen Quantum Dot SWIR Sensors

Allied Vision and QDI Systems recently announced a partnership to utilize Quantum Dot (QD) sensors. “The collaboration aims to accelerate the transition from traditional InGaAs-based SWIR sensors to a new class of QD sensors, delivering comparable performance while significantly improving cost, size, and weight characteristics. This breakthrough is expected to unlock new opportunities, particularly in industrial applications and drone-based imaging systems.”

What are quantum dots?

Per QDI Systems, quantum dots (QDs) are “tiny solution-processable semiconductor nanoparticles with extraordinary properties. […] At their tiny scale, quantum dots exhibit unique quantum properties, which distinguish them from larger particles. One of the most notable features of quantum dots is their size-dependent optoelectronic properties. This means that their ability to absorb and emit light can be precisely tuned by changing their size or composition.”

We’re just on the cusp of commercialized camera offerings utilizing QDs, but it’s apparent there may soon be choices beyond traditional InGaAs-only soluntion.

And that will further reduce the cost, size, and weight of SWIR cameras.

“Is SWIR Approaching Its CMOS Moment?”

Machine vision practitioners in the VIS space for more than a few years will recall that CCD sensors were dominant until 2007-ish and CCD still had significant market share for some years after that.

We submit that newer technologies like Sony SenSWIR and Quantum Dot SWIR sensors could, or really will, move SWIR from a niche technology into a mainstream machine vision tool

Choosing the Right SWIR Camera

If you are considering SWIR, we suggest you keep the following in mind. Or call us at 978-474-0044, and we’ll walk you through key choices based on your applications goals.

Area Scan vs. Line Scan
InGaAs vs. Sony SenSWIR vs. emerging Quantum Dot technologies
GigE, USB3, 5GigE, CoaXPress, Camera Link, and CSI-2 interfaces
Importance of selecting the proper camera, lens, lighting, and filters as a complete system

Or click the link for all SWIR cameras on offer at 1stVision.

Conclusion:

Stop asking, “Do I really need a SWIR camera?”

Start asking, “Is my visible-light camera hiding information that SWIR could reveal?”

Especially if you are into any of the following applications:

Moisture detection
Material discrimination
Plastic sorting
Recycling
Semiconductor inspection
Packaging verification
Food inspection

Wondering if SWIR Can Solve Your Vision Challenge?

1stVision has over 30 years of machine vision experience helping customers select the right SWIR cameras, optics, filters, lighting, and software.

Send us any/all of the following. More is better but we’ll work with what you’ve got:

Sample images
Working distance
Field of view
Inspection goals

and we’ll help determine if SWIR is the right technology.

#SWIR

September 29, 2026September 29, 2026

How to be successful in line scan applications

“A line scan system is only as good as its weakest component”

Granted, any system is only as good as its weakest component. But with some systems one can cut corners here or there and get away with it. With line scan mostly not. But the rewards of getting it right can deliver effective applications with excellent return on investment.

Some key line scan systems components – Photo credits clockwise from top left: Kowa, JAI, Shutterstock, Teledyne DALSA.

Success depends upon balancing all of the following:

Camera

Lens

Lighting

Motion system

Encoder

Mechanical stability

Image processing

Below we’ll look at each of these in turn.

Camera (and the sensor it contains)

One selects a line scan camera containing typically “one narrow row” of pixels. The row’s length is chosen to correspond to the width of the thing you are inspecting. The other dimension for the 2D application comes via the motion of the object past the camera – or vice versa.

Left: A representative line scan camera – Courtesy Teledyne DALSA

The “narrow row” may be 1, 2, or sometimes 3 or 4 pixels deep. A single pixel row offers the lowest cost, and can be sufficient for some applications. Adding a second or third pixel row, typically offset or staggered with respect to the adjacent row, offers improved resolution.

Line scan cameras may be monochrome or color in the visible (VIS) range. Or SWIR in the infrared portion of the spectrum. Or blended VIS-SWIR.

Right: Representative line scan camera front and back sides – Courtesy JAI.

There are several tens of line scan cameras from which to choose.

Lens

The lens focuses the target object onto your camera’s sensor, so the photons are captured in the pixel well and digitized for subsequent interpretation. Don’t cut corners on the lens.

Certain lenses are optimized for line scan applications. You may or may not need that. Call us at 978-474-0044 so we can help you make a good choice.

Left: LF high-resolution large-format machine vision lenses – Courtesy KOWA.

Lighting

Successful image processing depends on sufficient contrast to differentiate amongst details. While the sensor and the lens are critical, so too is lighting. In fact good lighting can sometimes permit using a “lesser” sensor or lens. Or achieving ambitious outcomes otherwise not possible without attention to lighting.

See our Knowledge Base Techbriefs on lighting. Each may be downloaded for free.

Motion system

As noted above in the camera segment, the other dimension for the 2D application comes via the motion of the object past the camera – or vice versa. This is the key aspect of line scan systems – there’s a never ending dimension in the direction of motion, and each image is a time-slice from that continuous movement.

One can move the “product” along a conveyer belt, “web” of spooled paper, etc., while holding the line scan camera and lighting fixed. Or one can move the line scan camera across the product, as with a flatbed scanner.

Ideally the motion should be continuous speed – or if variable use an encoder (see below). Capable of long use without a lot of frequent re-calibration beyond scheduled service maintenance. And with little variability in the third dimension – unless of course your application is a blend of line scan and height profiling, such as laser profiling.

Encoder

For variable speed product motion, e.g. conveyor belt with non-uniform speed during imaging, an encoder converts the rotary motion of a gear or transport wheel into electrical signals correlated to the speed of rotation.

Those signals can drive the shutter trigger on the camera.

Image royalty free – Courtesy Shutterstock.

Bonus blurb – calculating line rate

Related to how to make the whole system come together… and certainly tied to the motion system and encoder segments above, see our blog from some months ago on how to calculate line rate based on conveyor speed. That blog includes a downloadable spreadsheet with labeled fields and examples… and embedded formulas.

Mechanical stability

There’s no obvious graphic to show for this one, but one doesn’t have to be a mechanical or an optical engineer to get the point. Unplanned motion is highly undesirable, as all those lens and sensor choices are based upon a stable environment.

Sure, motion in one dimension is a given with line scan, but fast shutter takes care of that. But “wiggle” in the dimension of the pixel line, or height variability of the product being inspected – or of the line scan camera itself, introduce blur and uncertainty. Or the need to compensate for it.

Image processing

Machine vision is all about letting software interpret the image, adding value through speed, accuracy, tirelessness, and consistency. From camera configuration and control through image processing, you have a range of options.

There are camera manufacturer SDKs, third party software, and open source choices. And OS options including Windows, Linux, and various embedded systems platforms.

Sapera LT SDK windows – Courtesy Teledyne DALSA

#linescan