This is the released code for the following paper accepted to ACM Transactions on Graphics, presented at SIGGRAPH 2020
"Real-time Image Smoothing via Iterative Least Squares". Wei Liu, Pingping Zhang, Xiaolin Huang, Jie Yang, Chunhua Shen, Ian Reid. ACM Transactions on Graphics (TOG), 39(3), 1-24.
Requirements： CUDA library is required if running with the GPU version.
Usage:
The Test.m manuscript provides examples of the usage.
This script iterates over a hardcoded range of images (dataset dependend,
in the case of cutting tissues twice from 0 to 155) and applies a
sharpening algorithm to each image. The script initializes parameters like
lambda, iter, p, and eps, specifies the directory paths for input
and output, and processes each image in the range using the ILS_LNorm
function. The processed images are saved in the specified target directory.
In addition, the sharpen_chroma.m script demonstrates how to apply
sharpening to the chroma channel of an image. This script follows a
similar structure to Test.m, but includes additional steps to separate
the intensity and chromaticity components of the image, apply sharpening
to the chromaticity component, and then combine them back to create a
final RGB image. However, it is noted that the script currently lacks a
clamping step after combining the intensity and sharpened chromaticity
images, which can lead to incorrect values after converting back to RGB
in some cases. Adding a clamping step to ensure the RGB values are within
a valid range before saving the final image is recommended.
Related Work:
- "Semi-global weighted least squares in image filtering.", Wei Liu, Xiaogang Chen, Chuanhua Shen, Zhi Liu, and Jie Yang. In ICCV 2017. Code
- "Embedding bilateral filter in least squares for efficient edge-preserving image smoothing." Wei Liu, Pingping Zhang, Xiaogang Chen, Chunhua Shen, Xiaolin Huang, Jie Yang. IEEE Transactions on Circuits and Systems for Video Technology (2018). Code
- "A generalized framework for edge-preserving and structure-preserving image smoothing." Wei Liu, Pingping Zhang, Yinjie Lei, Xiaolin Huang, Jie Yang, and Michael Ng. IEEE Transactions on Transactions on Pattern Analysis and Machine Intelligence (2021). Code