-
Recent Posts
- Dimensionality reduction for image and texture set compression May 21, 2020
- “Optimizing” blue noise dithering – backpropagation through Fourier transform and sorting April 26, 2020
- Bilinear texture filtering – artifacts, alternatives, and frequency domain analysis April 14, 2020
- Using JAX, numpy, and optimization techniques to improve separable image filters March 15, 2020
- Separate your filters! Separability, SVD and low-rank approximation of 2D image processing filters February 3, 2020
Archives
- May 2020 (1)
- April 2020 (2)
- March 2020 (1)
- February 2020 (1)
- January 2020 (1)
- September 2019 (1)
- August 2019 (1)
- May 2018 (1)
- October 2017 (1)
- August 2017 (1)
- April 2017 (2)
- October 2016 (4)
- September 2016 (2)
- August 2016 (1)
- June 2016 (1)
- October 2015 (2)
- March 2015 (2)
- February 2015 (1)
- December 2014 (1)
- October 2014 (1)
- September 2014 (4)
- August 2014 (4)
- July 2014 (2)
- June 2014 (2)
- May 2014 (2)
- April 2014 (3)
- March 2014 (5)
- February 2014 (2)
- January 2014 (3)
Categories
Tag Archives: optimizations
Separate your filters! Separability, SVD and low-rank approximation of 2D image processing filters
In this blog post, I explore separable convolutional image filters: how can we check if a 2D filter is separable, and how to compute separable approximations to any arbitrary 2D filter represented in a numerical / matrix form using SVD. Continue reading
Posted in Code / Graphics
Tagged algorithms, approximation, blur, bokeh, depth of field, graphics, image processing, linear algebra, numpy, optimizations, postprocessing, python
8 Comments
