Lmst

From Julián Tachella @JulianTachella, posted on "Chi":

📰""Learning to reconstruct signals from binary measurements alone"📰

We present theory + a #selfsupervised approach for learning to reconstruct incomplete (!) and binary (!) measurements using the binary data itself. See the first figure and its alt-text.

https://arxiv.org/abs/2303.08691
with @lowrankjack
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The theory characterizes

- the best approximation of a set of signals from incomplete binary observations
- its sample complexity
- complements existing theory for signal recovery from binary measurements

See the third figure and its alt-text.
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The proposed self-supervised algorithm obtains performances on par with supervised learning and outperforms standard reconstruction techniques (such as binary iterative hard thresholding)

See the second figure and its alt-text.

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Code based on the deepinverse library is available at https://github.com/tachella/ssbm

Check out the paper for more details!

#SelfSupervisedLearning #CompressiveSensing #Quantization #InverseProblem #1bitcamera

On this figure, a description of a self-supervised method for learning an image reconstruction algorithm (taking as input its binary observations) when one has access only to binary observations of many images belonging to the same, low complexity signal set. The figure shows a sensing device on the left acquiring numerous binary observations that are feeding either a linear inversion method on a top branch (such as a pseudo inverse associated with the sensing model before binarisation) with poor image reconstruction quality shown on the top right of the figure, or, on a bottom branch, a neural network model learned from binary observations alone, by promoting consistency with the binary sensing model (more information in the paper). This second branch achieves higher quality in the estimated images, as shown on the bottom right of the figure.

On this figure, a table of images with 5 rows and 10 columns showing 10 images of the FashionMnist dataset (on the last row) reconstructed from their binary observations for 4 different algorithms, one per row, for 10 images of the dataset (such as a shoe, a shirt, ...). The 4 algorithms are: linear inversion, binary iterative hard thresholding (or BIHT, proposed in one bit compressive sensing), the proposed method using self-supervision, and a supervised method having access to the initial images to learn the reconstruction method. The proposed approach provides better quality than BIHT with a wavelet prior and the linear approach, and it is close to the image quality of the supervised method.

The Playdate is a modern handheld game console with a 1-bit black and white display. So it was just a matter of time before somebody developed a GameBoy Camera-like add-on for snapping similarly low-fi images. https://buff.ly/45c5ixB

#PlayDate #Camera #1BitCamera #PDCamera

#1bitcamera

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