×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer. (arXiv:2307.02625v2 [eess.IV] UPDATED)

Click here to flash read.

Images captured in poorly lit conditions are often corrupted by acquisition
noise. Leveraging recent advances in graph-based regularization, we propose a
fast Retinex-based restoration scheme that denoises and contrast-enhances an
image. Specifically, by Retinex theory we first assume that each image pixel is
a multiplication of its reflectance and illumination components. We next assume
that the reflectance and illumination components are piecewise constant (PWC)
and continuous piecewise planar (PWP) signals, which can be recovered via graph
Laplacian regularizer (GLR) and gradient graph Laplacian regularizer (GGLR)
respectively. We formulate quadratic objectives regularized by GLR and GGLR,
which are minimized alternately until convergence by solving linear systems --
with improved condition numbers via proposed preconditioners -- via conjugate
gradient (CG) efficiently. Experimental results show that our algorithm
achieves competitive visual image quality while reducing computation complexity
noticeably.

Click here to read this post out
ID: 291570; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: July 26, 2023, 7:31 a.m. Changes:
/u/anonymous
Dictionaries:
Words:
Spaces:
Views: 11
CC:
No creative common's license
Comments: