Pyramid Texture Filtering
Not on the map. Why?
Abstract (arXiv)
We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation -- the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 47 references that are in the map. First posted on arXiv 11 May 2023.
Builds on
5 of 47 references.
References · 5
- 2017Deep Bilateral Learning for Real-Time Image Enhancementcited by 200
- 2019A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothingcited by 13
- 2018Image Smoothing via Unsupervised Learningcited by 12
- 2020Real-time Image Smoothing via Iterative Least Squarescited by 8
- 2017Semi-Global Weighted Least Squares in Image Filteringcited by 5
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Generative Texture Filteringcited by 0
- 2026Generative Texture Filtering21 Apr 2026