Example-Based Image Synthesis
MIT 2001-07
Progress Report: January 1,
2002ÑJune 30, 2002
William T. Freeman
Project
Overview
The
collaboration has grown as we have learned more about each othersÕ research
interests. The initial topic
was example-based image synthesis:
synthesizing image detail using a database of example images. This synthesis can be of texture, as in
the Image Quilting technique, or of high-resolution image detail, as in
Super-resolution.
After
face-to-face meetings, we learned of a common research interest in shape
estimation from images. Mr. Sato
and Dr. Onozawa of NTT have developed laboratory equipment for analyzing object
images under varying optical conditions, useful for shape estimation. Prof. Freeman and Dr. Torralba of MIT
have developed a technique to improve shape estimates, using the image
information and the rendering parameters learned from the initial shape
estimate. These two research
components are a great fit. We
hope the NTT laboratory can use the technique and code we have developed, and
we look forward to applying our shape estimation method to their data.
Progress
Through June 2002
On
example-based image synthesis:
MIT graduate student Bryan Russell is seeking to extend example-based
super-resolution methods to video.
These methods use a database of examples of high- and low-resolution
patches of the same image data. A
Markov network model can assign the best high-resolution output patch for any
given low-resolution input patch, taking both the training examples, and
spatial context into account.
Application of this to video data would allow low-resolution moving
images to be displayed at high resolution.
The
problem for video data:
Inconsistent high-resolution details generated for different video
frames create objectionable flicker artifacts in the synthesized sequence. Our solution: Mr. Russell and post-doc Dr. Torralba developed an algorithm
to gate the synthesized high-resolution details by the amount of image change
at each location. This causes
static image regions to maintain constant high-resolution details, while the
details synthesized in moving regions are allowed to change. This substantially reduces flicker
artifacts, while adding high-resolution details to the moving images.
On
shape recipes: Prof. Freeman
and Dr. Torralba have developed a method to improve the accuracy of shape
estimates. The idea is to use the
initial shape estimate and the observed image to develop a local formula, or
ÒrecipeÓ, for going from high-resolution image detail to high-resolution shape
detail. This lets us exploit shape
details captured in the image, but not in the initial shape
reconstruction. We have illustrated
this method by improving upon initial stereo depth estimates for a variety of
objects. The stereo is accurate
enough to capture low-resolution shape details, but not high-resolution
ones. We implicitly learn the
material and lighting properties of the scene from the image and the
low-resolution shape details, learning a ÒrecipeÓ to go from the low-resolution
image to the low-resolution shape.
We then apply that recipe to the high-resolution image details to infer
high-resolution shape details. The
result adds high-resolution shape details visible in the image, but not
captured by the stereo.
We
have written-up a conference submission about this work, which we have provided
to Mr. Sato and Dr. Onozawa, and we have provided Mr. Sato with computer code.
Research
Plan for the Next Six Months
On
example-based image synthesis:
Mr. Russell plans to incorporate optical flow measurements to create
consistent high-resolution image detail even for patches of high image
motion. This should improve the
image quality of the synthesized high-resolution video data.
Mr.
Sato and Dr. Onozawa have expressed interest in the Image Quilting texture
synthesis method, for incorporation with their synthetic reality work. We will be able to give them that code
on a research basis, with approval from my former company. I am interested to help with any implementation
problems that arise.
On
shape recipes: At MIT, using
computer graphics simulations, we will explore properties of the shape recipes
method (when it works, when it breaks down, what order approximation is needed
for it to work well under general conditions). For explorations of the method, using real data, we hope to
use data from the laboratory set-up of Dr. Onozawa and Mr. Sato of NTT. We hope that Mr. Sato can use our
technique and code to further improve the shape estimates he acquires using his
optical imaging system.
We
expect to plan out these collaboration issues during Mr. SatoÕs visit to MIT on
August 19.