https://generativeai.pub/the-math-art-of-artist-0thernes-not-the-typical-96e009060bc1
"No Artist Tolerates Reality"
#aiart #ai_art #diffusion #denoising #latent_space #llm #dalle3 #firefly #grok #midjourney #krea #stable_diffusion #art #ai #artificial_intelligence #nft #prompting #prompts #chaos #improv #pinterest #instagram #DeviantArt #x #beeple
"To generate images, diffusion models use a process known as denoising. They convert an image into digital noise (an incoherent collection of pixels), then reassemble it. It’s like repeatedly putting a painting through a shredder until all you have left is a pile of fine dust, then patching the pieces back together. For years, researchers have wondered: If the models are just reassembling, then how does novelty come into the picture? It’s like reassembling your shredded painting into a completely new work of art.
Now two physicists have made a startling claim: It’s the technical imperfections in the denoising process itself that leads to the creativity of diffusion models. In a paper(opens a new tab) that will be presented at the International Conference on Machine Learning 2025, the duo developed a mathematical model of trained diffusion models to show that their so-called creativity is in fact a deterministic process — a direct, inevitable consequence of their architecture.
By illuminating the black box of diffusion models, the new research could have big implications for future AI research — and perhaps even for our understanding of human creativity. “The real strength of the paper is that it makes very accurate predictions of something very nontrivial,” said Luca Ambrogioni(opens a new tab), a computer scientist at Radboud University in the Netherlands."
https://www.quantamagazine.org/researchers-uncover-hidden-ingredients-behind-ai-creativity-20250630/
Aiarty Video Enhancer: Desktop AI Tool for Upscaling, Denoising & Deblurring – Faster Than Ever! https://petapixel.com/2025/06/13/aiarty-video-enhancer-desktop-ai-tool-for-upscaling-denoising-deblurring-faster-than-ever/ #VideoEnhancer #deblurring #Sponsored #Denoising #sponsored #upscaling #Software #editing #aiarty
Wait, three of the Technical Oscars went to different denoising algorithms? That seems like a lot. Maybe this should be a new category.
A fully automated, faster noise rejection approach to increasing the analytical capability of chemical imaging for digital histopathology.
PLoS ONE 14(4): e0205219, 2019
https://doi.org/10.1371/journal.pone.0205219
#denoising #openaccess
“Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance”
Perturbed-Attention Guidance (#PAG) é uma técnica sampling guidance de difusão que melhora a qualidade das amostras em cenários condicionais e incondicionais, sem necessidade de treino adicional ou integração de módulos externos. A PAG aprimora a estrutura das amostras sintetizadas durante o processo de #denoising, manipulando mapas de auto-atenção selecionados na #UNet de #difusão
🔗https://https://ku-cvlab.github.io/Perturbed-Attention-Guidance/
'On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models', by Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh.
http://jmlr.org/papers/v25/22-1120.html
#hessian #denoising #likelihood
'Lifted Bregman Training of Neural Networks', by Xiaoyu Wang, Martin Benning.
http://jmlr.org/papers/v24/22-0934.html
#autoencoders #classifiers #denoising
"Binlets: Data fusion-aware denoising enables accurate and unbiased quantification of multichannel signals", Silberberg & Grecco, 2023 https://www.sciencedirect.com/science/article/pii/S1566253523003159
Old school signal processing, not based on machine learning but instead on a translation-invariant Haar wavelet decomposition, profitably exploiting correlations across channels. The manuscript includes an accessible and brief "Theory" section and a longer appendix. All it needs to run is a test function between two data points.
In their benchmarks and use cases, the new method outperforms existing denoising methods. In both time series and on fluorescent microscopy images.
There's a repository available https://github.com/maurosilber/binlets and can be installed with `pip install binlets`.
Diffusion Models for Constrained Domains
Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson
Action editor: Rianne van den Berg.
IPOL published a new implementation of the algos behind multi-image #denoising #SuperResolution on the Google Pixel phones from a few generations ago:
https://www.ipol.im/pub/art/2023/460/
It's interesting to see these kinds of papers reproduced, even with a few years delay!
Now the PhD is done, I'm spending these days digitising old family footage (VHS and other tape formats) 🤓
Any tips to restore/upscale old footage with minimal loss? Here is an image showing the low resolution available.
I've tried #nlmeans #denoising but it removes detail. If there's any open source AI solution for #upscaling, please let me know!
#film #upscale #superresolution #denoiser #VHS
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
Mauricio Delbracio, Peyman Milanfar
Action editor: Jia-Bin Huang.
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
Mauricio Delbracio, Peyman Milanfar
Diffusion Models for Constrained Domains
Soft Diffusion: Score Matching with General Corruptions
Giannis Daras, Mauricio Delbracio, Hossein Talebi, Alex Dimakis, Peyman Milanfar
Action editor: Jonathan Scarlett.
#stableDiffusion #AI #txt2img
I have just finished this #experiment, in which 310 pictures have been generated with the same seed, only difference is the amount of samples, which increased progressively from 1 to 310.
These generations have then been upscaled using the realest-general-x4v3 upscaler model and #ComfyUI as software.
This the result:
https://youtu.be/hU4MyESWnm0
Posit AI Blog: De-noising Diffusion with torch https://triangleagency.co.uk/posit-ai-blog-de-noising-diffusion-with-torch/?utm_source=dlvr.it&utm_medium=mastodon #TheTriangleAgencyNews #Blog #Denoising #diffusion
Training Data Size Induced Double Descent For Denoising Feedforward Neural Networks and the Role ...
Rishi Sonthalia, Raj Rao Nadakuditi
TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation https://triangleagency.co.uk/tract-denoising-diffusion-models-with-transitive-closure-time-distillation/?utm_source=dlvr.it&utm_medium=mastodon #TheTriangleAgencyNews #Closure #Denoising #diffusion
