Machine Learning meets Computational Photography

I have already talked about the presentation by  Yair Weiss  this week, but back last December there were other equally interesting talks on the relationship between Machine Learning and Computational Photography, some even touched on some compressive sensing hardware .As a note I have to say I am impressed by how complex the transfer function is between sRGB and RAW (see Michael Brown's talk). Enjoy!

• From Image Restoration to Compressive Sampling in Computational Photography. A Bayesian Perspective. by Rafael Molina
• Old and New algorithm for Blind Deconvolution Yair Weiss
• The Light Field Camera: Extended Depth of Field, Aliasing and Superresolution by Paolo Favaro
• Efficient Regression for Computational Photography: From Color Management to Omnidirectional Superresolution by Maya Gupta
• Real-time Image Enhancement Using Edge-Optimized a-trous Wavelets by Hendrik Lensch
• Superresolution imaging - from equations to mobile applications by Filip Šroubek
• Modeling the Digital Camera Pipeline: From RAW to sRGB and Back by Michael Brown
• 2D and 3D Sparse Geometric Decomposition by Jean-Luc Starc

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