Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection

Details Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection

2010-02-17

arxiv.org/abs/1002.3344v1

Computer Science

Computer Vision and Pattern Recognition

Supplement to published work, on SSIM-optimized exact global histogram specification; please see arXiv:0901.0065

Scientific paper

The SSIM-optimized exact global histogram specification (EGHS) is shown to converge in the sense that the first order approximation of the result's quality (i.e., its structural similarity with input) does not decrease in an iteration, when the step size is small. Each iteration is composed of SSIM gradient ascent and basic EGHS with the specified target histogram. Selection of step size and other parameters is also discussed.

Affiliated with

Also associated with

No associations

Rating

Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-52417