Pyramid Texture Filtering
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling
PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality
HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
Learning to See in the Extremely Dark
RT-X Net: RGB-Thermal cross attention network for Low-Light Image Enhancement
토론
이 논문과 연결된 포럼 글이 아직 없습니다.