Many slides from Steve Seitz and Larry Zitnick

Slides:



Advertisements
Similar presentations
Linear Filtering – Part II Selim Aksoy Department of Computer Engineering Bilkent University
Advertisements

Image Processing. Overview ImagesPixel Filters Neighborhood Filters Dithering.
Linear Filtering – Part II Selim Aksoy Department of Computer Engineering Bilkent University
Linear Filtering – Part II Selim Aksoy Department of Computer Engineering Bilkent University
Image Interpolation CS4670: Computer Vision Noah Snavely.
Ted Adelson’s checkerboard illusion. Motion illusion, rotating snakes.
Slides from Alexei Efros
Linear Filtering – Part II Selim Aksoy Department of Computer Engineering Bilkent University
Filtering CSE P 576 Larry Zitnick
Image Filtering, Part 2: Resampling Today’s readings Forsyth & Ponce, chapters Forsyth & Ponce –
Convolution, Edge Detection, Sampling : Computational Photography Alexei Efros, CMU, Fall 2006 Some slides from Steve Seitz.
Sampling and Pyramids : Rendering and Image Processing Alexei Efros …with lots of slides from Steve Seitz.
Edges and Scale Today’s reading Cipolla & Gee on edge detection (available online)Cipolla & Gee on edge detection Szeliski – From Sandlot ScienceSandlot.
Fourier Transform Analytic geometry gives a coordinate system for describing geometric objects. Fourier transform gives a coordinate system for functions.
Lecture 2: Edge detection and resampling
Image transformations, Part 2 Prof. Noah Snavely CS1114
Image Sampling Moire patterns
Lecture 3: Edge detection, continued
CSCE 641 Computer Graphics: Fourier Transform Jinxiang Chai.
Lecture 4: Image Resampling CS4670: Computer Vision Noah Snavely.
Lecture 3: Image Resampling CS4670: Computer Vision Noah Snavely Nearest-neighbor interpolation Input image 3x upsample hq3x interpolation (ZSNES)
CPSC 641 Computer Graphics: Fourier Transform Jinxiang Chai.
Fourier Theory and its Application to Vision
Computational Photography: Fourier Transform Jinxiang Chai.
Slides from Alexei Efros, Steve Marschner Filters & fourier theory.
Image Sampling Moire patterns -
Fourier Analysis : Rendering and Image Processing Alexei Efros.
Image Sampling CSE 455 Ali Farhadi Many slides from Steve Seitz and Larry Zitnick.
Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5409 T-R 10:30am – 11:50am.
Applications of Image Filters Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem 02/04/10.
Image Resampling CS4670: Computer Vision Noah Snavely.
Image Resampling CS4670: Computer Vision Noah Snavely.
Lecture 4: Image Resampling and Reconstruction CS4670: Computer Vision Kavita Bala.
Image Processing Edge detection Filtering: Noise suppresion.
Lecture 3: Edge detection CS4670/5670: Computer Vision Kavita Bala From Sandlot ScienceSandlot Science.
Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm.
GEOMETRIC OPERATIONS. Transformations and directions Affine (linear) transformations Translation, rotation and scaling Non linear (Warping transformations)
Brent M. Dingle, Ph.D Game Design and Development Program Mathematics, Statistics and Computer Science University of Wisconsin - Stout Image Processing.
Lecture 5: Fourier and Pyramids
Projects Project 1a due this Friday Project 1b will go out on Friday to be done in pairs start looking for a partner now.
Last Lecture photomatix.com. Today Image Processing: from basic concepts to latest techniques Filtering Edge detection Re-sampling and aliasing Image.
Images and Filters CSEP 576 Ali Farhadi Many slides from Steve Seitz and Larry Zitnick.
Antialiasing. What is alias? Alias - A false signal in telecommunication links from beats between signal frequency and sampling frequency (from dictionary.com)
Prof. Adriana Kovashka University of Pittsburgh September 14, 2016
Image Resampling & Interpolation
Miguel Tavares Coimbra
Linear Filtering – Part II
Image Sampling Moire patterns
Department of Computer Engineering
Image transformations
Gonzalez & Woods, and Efros)
Image transformations
Fourier Transform Analytic geometry gives a coordinate system for describing geometric objects. Fourier transform gives a coordinate system for functions.
Fourier Transform Analytic geometry gives a coordinate system for describing geometric objects. Fourier transform gives a coordinate system for functions.
Image Sampling Moire patterns
More Image Manipulation
CSCE 643 Computer Vision: Thinking in Frequency
Samuel Cheng Slide credits: Noah Snavely
Image Processing Today’s readings For Monday
Filtering Part 2: Image Sampling
Linear filtering.
Image Sampling Moire patterns
Lecture 5: Resampling, Compositing, and Filtering Li Zhang Spring 2008
Oh, no, wait, sorry, I meant, welcome to the implementation of 20th-century science fiction literature, or a small part of it, where we will learn about.
Department of Computer Engineering
Fourier Transform Analytic geometry gives a coordinate system for describing geometric objects. Fourier transform gives a coordinate system for functions.
Image Resampling & Interpolation
Resampling.
Samuel Cheng Slide credits: Noah Snavely
Miguel Tavares Coimbra
Presentation transcript:

Many slides from Steve Seitz and Larry Zitnick Image Sampling CSE 576 Ali Farhadi Many slides from Steve Seitz and Larry Zitnick

Image Sampling F( ) = F( ) =

Image Scaling This image is too big to fit on the screen. How can we reduce it? How to generate a half- sized version?

Image sub-sampling 1/8 1/4 Throw away every other row and column to create a 1/2 size image - called image sub-sampling

Why does this look so crufty? Image sub-sampling 1/2 1/4 (2x zoom) 1/8 (4x zoom) Why does this look so crufty?

Down-sampling Aliasing can arise when you sample a continuous signal or image occurs when your sampling rate is not high enough to capture the amount of detail in your image Can give you the wrong signal/image—an alias formally, the image contains structure at different scales called “frequencies” in the Fourier domain the sampling rate must be high enough to capture the highest frequency in the image

Sampling and the Nyquist rate Aliasing can arise when you sample a continuous signal or image occurs when your sampling rate is not high enough to capture the amount of detail in your image Can give you the wrong signal/image—an alias formally, the image contains structure at different scales called “frequencies” in the Fourier domain the sampling rate must be high enough to capture the highest frequency in the image To avoid aliasing: sampling rate ≥ 2 * max frequency in the image said another way: ≥ two samples per cycle This minimum sampling rate is called the Nyquist rate

2D example Good sampling Bad sampling

Subsampling with Gaussian pre-filtering Solution: filter the image, then subsample Filter size should double for each ½ size reduction. Why?

Subsampling with Gaussian pre-filtering Solution: filter the image, then subsample Filter size should double for each ½ size reduction. Why? How can we speed this up?

Compare with... 1/2 1/4 (2x zoom) 1/8 (4x zoom)

Moire patterns in real-world images Moire patterns in real-world images. Here are comparison images by Dave Etchells of Imaging Resource using the Canon D60 (with an antialias filter) and the Sigma SD-9 (which has no antialias filter). The bands below the fur in the image at right are the kinds of artifacts that appear in images when no antialias filter is used. Sigma chose to eliminate the filter to get more sharpness, but the resulting apparent detail may or may not reflect features in the image.

More examples Check out Moire patterns on the web.

Up-sampling How do we compute the values of pixels at fractional positions?

Up-sampling How do we compute the values of pixels at fractional positions? Bilinear sampling: f (x,y) f (x+1,y) f (x+0.8,y+0.3) f (x + a, y + b) = (1 - a)(1 - b) f (x, y) + a(1 - b) f (x + 1, y) + (1 - a)b f (x,y + 1) + ab f (x + 1, y + 1) f (x,y+1) f (x+1,y+1) Bicubic sampling fits a higher order function using a larger area of support.

Up-sampling Methods

Up-sampling Nearest neighbor Bilinear Bicubic

Up-sampling Nearest neighbor Bilinear Bicubic