Did you ever take a photo & wish you'd zoomed in more or framed better? When this happens, we just crop.
Now there's a better way: Zoom Enhance -a new feature my team just shipped on Pixel. Available in Google Photos under Tools, it enhances both zoomed & un-zoomed images
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Zoom Enhance is our first im-to-im diffusion model designed & optimized to run fully on-device. It allows you to crop or frame the shot you wanted, and enhance it -after capture. The input can be from any device, Pixel or not, old or new. Below are some examples & use cases
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Let's say you've zoomed to the max on your Pixel 8/9 Pro and got your shot; but you wish you could get a little closer. Now you can zoom in more, and enhance.
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A bridge too far to see the details? A simple crop may not give the quality you want. Zoom Enhance can come in handy.
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If you've been to the Louvre you know how hard it is to get close to the most famous painting of all time.
Next time you could shoot with the best optical quality you have (5x in this case), then zoom in after the fact.
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Maybe you're too far away to read a sign and can use a little help from Zoom Enhance
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Like most people, I have lots of nice shots that can be even nicer if I'd framed them better. Rather than just cropping, you can now frame the shot you wanted, after the fact, and without losing out on quality.
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Is the subject small and the field of view large? Zoom Enhance can help to isolate and enhance the region of interest.
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Sometimes there's one or more better shots hiding within the just-average shot you took. Compose your best shot and enhance.
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There's a lot of gems hidden in older, lower quality photos that you can now isolate and enhance. Like this one from some 20 years ago.
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Pictures you get on social media or on the web (or even your own older photos) may not always be high quality/resolution. If they're small enough (~1MP), you can enhance them with or without cropping.
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So Zoom Enhance gives you the freedom to capture the details within your photos, allowing you to highlight specific elements and focus on what matters to you.
It's a 1st step in powerful editing tools for consumer images, harnessing on-device diffusion models.
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Bonus use case worth mentioning:
Using your favorite text-2-image generator you typically get a result ~1 MP resolution (left image is 1280 × 720). If you want higher resolution, you can directly upscale on-device (right, 2048 × 1152) with Zoom Enhance.
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Tweedie's formula is super important in diffusion models & is also one of the cornerstones of empirical Bayes methods.
Given how easy it is to derive, it's surprising how recently it was discovered ('50s). It was published a while later when Tweedie wrote Stein about it
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The MMSE denoiser is known to be the conditional mean f̂(y) = 𝔼(x|y). In this case, we can write the expression for this conditional mean explicitly:
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Note that the normalizing term in the denominator is the marginal density of y.
Images aren’t arbitrary collections of pixels -they have complicated structure, even small ones. That’s why it’s hard to generate images well. Let me give you an idea:
3×3 gray images represented as points in ℝ⁹ lie approximately on a 2-D manifold: the Klein bottle!
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Images can be thought of as vectors in high-dim. It’s been long hypothesized that images live on low-dim manifolds (hence manifold learning). It’s a reasonable assumption: images of the world are not arbitrary. The low-dim structure arises due to physical constraints & laws
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But this doesn’t mean the “low-dimensional” manifold has a simple or intuitive structure -even for tiny images. This classic paper by Gunnar Carlsson gives a lovely overview of the structure of data generally (and images in particular). Worthwhile reading.
Michael Jordan gave a short, excellent, and provocative talk recently in Paris - here's a few key ideas
- It's all just machine learning (ML) - the AI moniker is hype
- The late Dave Rumelhart should've received a Nobel prize for his early ideas on making backprop work
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The "Silicon Valley Fever Dream" is that data will create knowledge, which will lead to super intelligence, and a bunch of people will get very rich.....
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.... yet the true value of technologies like LLMs is that we're getting the benefit of interacting with the collective knowledge of many many individuals - it's not that we will produce one single uber-intelligent being
How are Kernel Smoothing in statistics, Data-Adaptive Filters in image processing, and Attention in Machine Learning related?
I wrote a thread about this late last year. I'll repeat it here and include a link to the slides at the end of the thread.
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In the beginning there was Kernel Regression - a powerful and flexible way to fit an implicit function point-wise to samples. The classic KR is based on interpolation kernels that are a function of the position (x) of the samples and not on the values (y) of the samples.
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Instead of a fixed smoothing parameter h, we can adjusted it dynamically based on the local density of samples near the point of interest. This enables accounting for variations in the spatial distribution of samples, but doesn't take into account of the values of samples
Years ago when my wife and I we were planning to buy a home, my dad stunned me with a quick mental calculation of loan payments.
I asked him how - he said he'd learned the strange formula for compound interest from his father, who was a merchant in 19th century Iran.
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The origins of the formula my dad knew is a mystery, but I know it has been used in the bazaar's of Iran (and elsewhere) for as long as anyone can remember
It has an advantage: it's very easy to compute on an abacus. The exact compounding formula is much more complicated
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I figured out how the two formulae relate: the historical formula is the Taylor series of the exact formula around r=0.
But the crazy thing is that the old Persian formula goes back 100s (maybe 1000s) of years before Taylor's, having been passed down for generations
How are Kernel Smoothing in statistics, Data-Adaptive Filters in image processing, and Attention in Machine Learning related?
My goal is not to argue who should get credit for what, but to show a progression of closely related ideas over time and across neighboring fields.
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In the beginning there was Kernel Regression - a powerful and flexible way to fit an implicit function point-wise to samples. The classic KR is based on interpolation kernels that are a function of the position (x) of the samples and not on the values (y) of the samples.
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Instead of a fixed smoothing parameter h, we can adjusted it dynamically based on the local density of samples near the point of interest. This enables accounting for variations in the spatial distribution of samples, but doesn't take into account of the values of samples