Multi-Focus Image Fusion and Depth Map Estimation Based on Iterative Region Splitting Techniques

In this paper, a multi-focus image stack captured by varying positions of the imaging plane is processed to synthesize an all-in-focus (AIF) image and estimate its corresponding depth map. Compared with traditional methods (e.g., pixel- and block-based techniques), our focus-based measures are calculated based on irregularly shaped regions that have been refined or split in an iterative manner, to adapt to different image contents. An initial all-focus image is first computed, which is then segmented to get a region map. Spatial-focal property for each region is then analyzed to determine whether a region should be iteratively split into sub-regions. After iterative splitting, the final region map is used to perform regionally best focusing, based on the Winner-take-all (WTA) strategy, i.e., choosing the best focused pixels from image stack. The depth image can be easily converted from the resulting label image, where the label for each pixel represents the image index from which the pixel with the best focus is chosen. Regions whose focus profiles are not confident in getting a winner of the best focus will resort to spatial propagation from neighboring confident regions. Our experiments show that the adaptive region-splitting algorithm outperforms other state-of-the-art methods or commercial software in synthesis quality (in terms of a well-known Q metric), depth maps (in terms of subjective quality), and processing speed (with a gain of 17.81~40.43%).

Medienart:

E-Artikel

Erscheinungsjahr:

2019

Erschienen:

2019

Enthalten in:

Zur Gesamtaufnahme - volume:5

Enthalten in:

Journal of imaging - 5(2019), 9 vom: 02. Sept.

Sprache:

Englisch

Beteiligte Personen:

Lie, Wen-Nung [VerfasserIn]
Ho, Chia-Che [VerfasserIn]

Links:

Volltext

Themen:

All-in-focus
Depth from focus
Depth image
Image fusion
Journal Article
Multi-focus

Anmerkungen:

Date Revised 03.09.2021

published: Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.3390/jimaging5090073

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM330034316