Introducing Ceph-O-Vision

I’ve been interested in cephalopod vision ever since I learned that, despite their superb appreciation for chroma (as evidenced by their ability to match the color of their surroundings as well as texture and pattern), cuttlefish eyes contain only one light-sensitive pigment. Unlike ourselves and other multichromatic animals that perceive color as a mix of activations of different-colored light receptors, cuttlefish must have another way. So while the images coming into the brain of a cuttlefish might look something like this . . .


. . . they manage to interpret the images to precisely match their surroundings and communicate colorful displays to other cuttlefish. Some time ago Stubbs and Stubbs put forth the possibility that they might use chromatic aberrations to interpret color (I discussed and simulated what that might look like in this post). What looks like random flickering in the gif above is actually simulated focusing across chromatic aberrations. [original video]. Contrary to what one might think, defocus and aberration in images isn’t “wrong.” On the contrary, if you know how to interpret them they provide a wealth of information that might allow a cuttlefish to see the world in all its chromatic glory.

Top: learned color image based on chromatic aberration stack. Middle: Neural network color reconstitution Bottom: Ground truth color image

We shouldn’t expect the cuttlefish to experience their world in fuzzy grayscale any more than we should expect humans to perceive their world in an animal version of a Bayer area, each photoreceptor individually distinguished (not to mention distracting saccades, blind spot at the optic nerve, vasculature shadowing, etc.). Instead, just like us humans, they would learn to perceive the visual data produced by their optical system in whatever way makes the most sense and is most useful.

I piped simulated cuttlefish vision images into a convolutional neural network with corresponding color images as reference. The cuttle-vision images flow through the 7 layer network and are compared to the RGB targets on the other side. I started by building a dataset of simulated images consisting of randomly placed pixel-sized colored dots. This was supposed to be the easy “toy example” I started with before moving on to real images.

Left: training input, middle: network’s attempt at reconstitution, right: target. For pixel sized color features, the convolutional kernels of the network learn to blur the target pixels into ring shapes.

Bizarrely, the network learned to interpret these images as colored donuts, usually centered around the correct location but incapable of reconstituting the original layout. Contrary to what you might expect, the simple dataset performed poorly even with many training examples and color image reconstitution improved dramatically when I switched to real images. Training on a selection of landscape images looks something like this:

Center: Ceph-O-Vision color perception. Bottom: Ground truth RGB. Top: Chromatic aberration training images (stacked as a color image for viewing)

As we saw in first example, reconstituting sparse single pixels from chromatic aberration images trains very poorly. However, the network was able to learn random patterns of larger features (offering better local context) much more effectively:

Interestingly enough, the network learns to be most sensitive to edges. You can see in the training gif above that after 1024 epochs of training, the network mainly reconstitutes pattern edges. It never learns to exactly replicate the RGB pattern, but gets pretty close. It would be interesting to use a network like this to predict what sort of optical illusions a cuttlefish might be susceptible too. This could provide a way to test the chromatic aberration hypothesis in cephalopod vision. Wikipedia Imageby Hans Hillewaert used as a mask for randomly generated color patterns.

Finally, I trained the network on some footage of a hunting cuttlefish CC BY SA John Turnbull. Training on the full video, here’s what a single frame looks like as the network updates over about a thousand training epochs:

This project is far from a finished piece, but it’s already vastly improved my intuition for how convolutional neural networks interpret images. It also provides an interesting starting point for thinking about how cuttlefish visually communicate and perceive. If you want more of the technical and unpolished details, you can follow this project’s Github repository. I have a lot of ideas on what to try next: naturally some control training with a round pupil (and thus less chromatic aberration), but also to compare the simple network I’ve built so far to the neuroanatomy of cephalopods and to implement a “smart camera” version for learning in real-time. If you found this project interesting, or have your own cool ideas mixing CNNs and animal vision, be sure to let me know @theScinder or in the comments.


Through the strange eyes of a cuttlefish

A classic teaching example in black and white film photography courses is the tomato on a bed of leaves. Without the use of a color filter, the resulting image is low-contrast and visually un-interesting. The tomato is likely to look unnaturally dark and lifeless next to similarly dark leaves; although in a color photograph the colors make for a stark contrast, in fact the intensity values of the red and green of tomato fruit and leaves are nearly the same. The use of a red or green filter can attenuate the intensity of one of the colors, making it possible for an eager photographer to undertake the glamorous pursuit of fine-art salad photography.


The always clever cephalopods (smart enough to make honorary vertebrate status in UK scientific research) somehow manage to pull off a similar trick without the use of a photographer’s color filters. Marine biologists have been flummoxed for years by the ability of squid, cuttlefish, and octopuses* to effect exact color camouflage in complex environments, and their impressive use of color patterning in hunting and inter-species communication. The paradox is that their eyes (cephalopods, not marine biologists) only contain a single type of photoreceptor, rather than the two or more different color photoreceptors of humans and other color sensitive animals.

Berkeley/Harvard duo Stubbs & Son have put forth a plausible explanation for the age-old paradox of color camouflage in color-blind cephalopods. They posit that cephalopods use chromatic aberration and a unique pupil shape to distinguish colors. With a wide, w-shaped pupil, cephalopods potentially retain much of the color blurring of different wavelengths of light. Chromatic aberration is nothing more than color-dependent defocus, and by focusing through the different colors it is theoretically possible for the many-limbed head-foots to use their aberrated eyes as an effective spectrophotometer, using a different eye length to sharply focus each color. A cuttlefish may distinguish tomato and lettuce in a very different way than a black and white film camera or human eyes.


A cuttlefish’s take on salad

A cuttlefish might focus each wavelength sequentially to discern color. In the example above, each image represents preferential focus for red, green, and blue from top to bottom. By comparing each image to every other image, the cephalopod could learn to distinguish the colorful expressions of their friends, foes, and environment. Much like our own visual system automatically filters and categorizes objects in a field of view before we know it, much of this perception likely occurs at the level of “pre-processing,” before the animal is acutely aware of how they are seeing.


How a cuttlefish might see itself


A view of the reef.

A typical night out through the eyes of a cuttlefish might look something like this:

There are distinct advantages to this type of vision in specialized contexts. Using only one type of photoreceptor, light sensitivity is increased compared to the same eye with multiple types of photoreceptors (ever notice how human color acuity falls off at night?) Mixed colors would look distinctly different, and, potentially, individual pure wavelength could be more accurately distinguished. In human vision we can’t tell the difference between an individual wavelength and a mix of colors that happen to excite our color photoreceptors in the same proportions as the pure color, but a cuttlefish might be able to resolve these differences.

On the other hand, the odd w-shaped pupil of cephalopods retains more imaging aberrations in than a circular pupil (check out the dependence of aberrations on the pupil radius in the corresponding Zernike polynomials to understand why). As a result, cephalopods would have slightly worse vision in some conditions as compared to humans with the same eye size. Mainly those conditions consist of living on land. Human eye underwater are not well-suited to the higher refractive index of water as compared to air. We would also probably need to incorporate some sort of lens hood (e.g. something like a brimmed hat) to deal with the strong gradient of light formed from light absorption in the water, another function of the w-shaped cephalopod pupil.

Studying the sensory lives of other organisms provides insight into how they might think, illuminating our own vision and nature of thought by contrast. We may still be a long ways off from understanding how it feels to instantly change the color and texture of one’s skin, but humans have just opened a small aperture into the minds of cuttlefish to increase our understanding of the nature of thought and experience.

How I did it
Ever image is formed by smearing light from a scene according to the Point Spread Function (PSF) of the imaging system. This is a consequence of the wave nature of light and the origins of the diffraction limit. In Fourier optics, the point spread function is the absolute value squared of the pupil function. To generate the PSF, I thresholded and dilated this image of a common cuttlefish eye (public domain from Wikipedia user FireFly5), before taking the Fourier transform and squaring the result. To generate the images and video mentioned above, I added differential defocus (using the Zernike polynomial for defocus) to each color channel and cycled through the result three monochromatic images. I used ImageJ and octave for image processing.

Sources for original images in order of appearance:

And Movie S2

*The plural of octopus has all the makings of another senseless ghif/gif/zhaif controversy. I have even heard one enthusiast insist on “octopodes”

Bonus Content:


Primary color disks.

In particular, defocus pseudocolor vision would make for interesting perceptions of mixed wavelengths. Observe the color disks above (especially the edges) in trichromatic and defocus pseudo-color.



The aperture used to calculate chromatic defocus.

Bonus content original image sources:

Swimming cuttlefish in camouflage CC SA BY Wikipedia user Konyali43 available at:

The aperture I used for computing chromatic defocus is a mask made from the same image as the top image for this post:

2017/05/03 – Fixed broken link to Stubbs & Stubbs PNAS paper: