NeuralCam is a photo app for the iPhone that uses computational photography and machine learning to take bright photos in low light settings. It combines multiple underexposed photos taken in quick succession to reduce noise and increase detail and brightness. The photos are aligned, patches are merged using a reference frame, and a deep learning model is used to brighten the composite image while controlling noise and color accuracy. The founders plan to expand the technology to additional features like night video and day mode enhancement.

1. NeuralCam
Night Mode app for all iPhones, all cameras

2. Hello!
Szabi Szekely
Co-founder, NeuralCam

3. What is NeuralCam?
A photo app designed specifically
for the iPhone, which allows you
to take bright photos in low light
settings.

5. Story
● Co-founded Halcyon Mobile roughly 15 years ago (mobile app design
and development)
● Working for clients like SigFig, SmartUp, Airstar, LEGO, Redbull
● But also working on our own products (MyPersonalGame, Dollarbird,
MimeChat, NeuralCam, etc.)
● Started an AI lab in 2016 (idea: do deep tech from Transylvania)
○ Self-organizing notes (NLP, word embeddings)
○ Phone as smart security camera, trainable to recognize
anything
○ Brick detector for Lego sets
○ Lots of other experiments

6. What sparked the idea of NeuralCam
● We experimented with deep learning, especially image
processing
● Early this year we started on a night mode pipeline
● Inspired by Google’s Night Sight technology
● Market need for the iPhone users (800 million)
Apps + UX + AI + Market need

7. The technology behind it
What does it actually do?

8. 100% crop
iPhone XR iPhone XR w/ NeuralCam

9. 100% crop
iPhone XR iPhone XR w/ NeuralCam

10. The problem with small sensors in low light
● Phone camera sensor vs DSLR
● More noise
● Less overall image quality
● Can’t just brighten it up…

11. The solution: computational photography
● Combine multiple photos (= more information)
● Which reduces noise in the dark areas
● So that it can be brightened with ML
● And results in a better quality image
● With much better noise control and color accuracy

12. Easier said than done :)
● Hand movement
● Moving objects
● Light changes between frames
● Needs smart alignment

13. The steps
● Image Collection
● Image Alignment
● Smart Merging
● Brightening (machine learning solution)
● Post processing

14. Image Collection
● Auto-exposure algorithm to set the right exposure
(duration and ISO), long enough to let in light but short
enough not to blur moving objects
● Multiple images of the same scene
● Customized ISO settings for different devices

15. Image Alignment
● Using optical image stabilization
● Select the sharpest frame as base frame
● Highly misaligned frames are discarded
● Successfully aligned frames are smartly merged into
the base frame

16. Smart Merging
● Picking a reference image
● Splitting images into smaller patches
● Merging the patches into a high quality
patch
● Reconstructing the image

17. Brightening
● The previous steps result in a higher quality but still
dark image
● Brightening an image nicely is a hard problem (different
areas need different levels of brightening)
● Deep Learning to the rescue: a model trained on a
combination of synthetic and real-world data (dark -
bright image pairs) works nicely
Machine Learning solution

19. Post-processing
Custom post processing steps to correct issues specific
to night photos.

24. Some stats
● More than 2.5 million photos taken
● 80% of our users have an iPhone X or newer
● Users spend an average of 2.6 minutes per day taking
photos with NeuralCam
● That’s roughly 420 hours/day

25. Talking about the future
● We’re just getting started: we see a huge opportunity
in Deep Learning + Image Processing as a core
technology for many different products
● Night Video
● Day Mode
● Automated Professional Photo Editing
● And a lot more

26. Questions?

27. Thank you!
Szabi Szekely
szabi@neural.cam