Update README.md

README.md

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 <sup>1</sup> University of Wisconsin-Madison <sup>2</sup> Snap, Inc<br>
 
 
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 ![color_spads](assets/README_teaser_color_SPAD.png)
 
 
-Abstract:
-Capturing high-quality images from only a few detected photons is a fundamental challenge in computational imaging. 
-Single-photon avalanche diode (SPAD) sensors promise high-quality imaging in regimes where conventional cameras fail, but raw *quanta frames* contain only sparse, noisy, binary photon detections. 
-Recovering a coherent image from a burst of such frames requires handling alignment, denoising, and demosaicing (for color) under noise statistics far outside those assumed by standard restoration pipelines or modern generative models. 
-We present an approach that adapts large text-to-image latent diffusion models to the photon-limited domain of quanta burst imaging. 
-Our method leverages the structural and semantic priors of internet-scale diffusion models while introducing mechanisms to handle Bernoulli photon statistics. 
-By integrating latent-space restoration with burst-level spatio-temporal reasoning, our approach produces reconstructions that are both photometrically faithful and perceptually pleasing, even under high-speed motion. 
-We evaluate the method on synthetic benchmarks and new real-world datasets, including the first color SPAD burst dataset and a challenging *Extreme-Deforming (XD)* video benchmark. 
-Across all settings, the approach substantially improves perceptual quality over classical and modern learning-based baselines, demonstrating the promise of adapting large generative priors to extreme photon-limited sensing. 
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+## All model weights are available here now! 
 
 Code at [github.com/Aryan-Garg/gQIR](https://github.com/Aryan-Garg/gQIR).