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Mutually Guided Image Filtering

Filtering images is required by numerous multimedia, computer vision and graphics tasks. Despite diverse goals of different tasks, making effective rules is key to the filtering performance. Linear translation-invariant filters with manually designed kernels have been widely used. However, their performance suffers from content-blindness. To mitigate the content-blindness, a family of filters, called joint/guided filters, have attracted a great amount of attention from the community. The main drawback of most joint/guided filters comes from the ignorance of structural inconsistency between the reference and target signals like color, infrared, and depth images captured under different conditions. Simply adopting such guidelines very likely leads to unsatisfactory results. To address the above issues, this paper designs a simple yet effective filter, named mutually guided image filter (muGIF), which jointly preserves mutual structures, avoids misleading from inconsistent structures and smooths flat regions. The proposed muGIF is very flexible, which can work in various modes including dynamic only (self-guided), static/dynamic (reference-guided) and dynamic/dynamic (mutually guided) modes. Although the objective of muGIF is in nature non-convex, by subtly decomposing the objective, we can solve it effectively and efficiently. The advantages of muGIF in effectiveness and flexibility are demonstrated over other state-of-the-art alternatives on a variety of applications. Our code is publicly available at https://sites.google.com/view/xjguo/mugif.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.42 No.3 March 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 694 - 707

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

IMAGE FILTERING
JOINT IMAGE FILTERING
GUIDED IMAGE FILTERING
MUTUALLY GUIDED IMAGE FILTERING

Info Detail Spesifik

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Pernyataan Tanggungjawab

Xiaojie Guo, Yu Li, Jiayi Ma, Haibin Ling

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