Multi-focus Image Fusion

Deep Learning-Based Multi-Focus Image Fusion: A Survey and a Comparative Study

Xingchen Zhang

Imperial College London

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Multi-focus image fusion (MFIF) is an important area in image processing. Since 2017, deep learning has been introduced to the field of MFIF and various methods have been proposed. However, there is a lack of survey papers that discuss deep learning-based MFIF methods in detail. In this study, we fill this gap by giving a detailed survey on deep learning-based MFIF algorithms, including methods, datasets and evaluation metrics. To the best of our knowledge, this is the first survey paper that focuses on deep learning-based approaches in the field of MFIF. Besides, extensive experiments have been conducted to compare the performance of deep learning-based MFIF algorithms with conventional MFIF approaches. By analyzing qualitative and quantitative results, we give some observations on the current status of MFIF and discuss some future prospects of this field.

Multi-focus Image Fusion: A Benchmark

Xingchen Zhang

Imperial College London

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Multi-focus image fusion (MFIF) has attracted considerable interests due to its numerous applications. While much progress has been made in recent years with efforts on developing various MFIF algorithms, some issues significantly hinder the fair and comprehensive performance comparison of MFIF methods, such as the lack of large-scale test set and the random choices of objective evaluation metrics in the literature. To solve these issues, this paper presents a multi-focus image fusion benchmark (MFIFB) which consists a test set of 105 image pairs, a code library of 30 MFIF algorithms, and 20 evaluation metrics. MFIFB is the first benchmark in the field of MFIF and provides the community a platform to compare MFIF algorithms fairly and comprehensively. Extensive experiments have been conducted using the proposed MFIFB to understand the performance of these algorithms. By analyzing the experimental results, effective MFIF algorithms are identified. More importantly, some observations on the status of the MFIF field are given, which can help to understand this field better.

Multi-focus Image Fusion: A Survey of the State of the Art

Yu Liu, Lei Wang, Juan Cheng, Chang Li, Xun Chen

Hefei University of Technology; University of Science and Technology of China

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Multi-focus image fusion is an effective technique to extend the depth-of-field of optical lenses by creating an all-in-focus image from a set of partially focused images of the same scene. In the last few years, great progress has been achieved in this field along with the rapid development of image representation theories and approaches such as multi-scale geometric analysis, sparse representation, deep learning, etc. This survey paper first presents a comprehensive overview of existing multi-focus image fusion methods. To keep up with the latest development in this field, a new taxonomy is introduced to classify existing methods into four main categories: transform domain methods, spatial domain methods, methods combining transform domain and spatial domain, and deep learning methods. For each category, representative fusion methods are introduced and summarized. Then, a comparative study for 18 representative fusion methods is conducted based on 30 pairs of commonly-used multi-focus images and 8 popular objective fusion metrics. All the relevant resources including source images, objective metrics and fusion results are released online, aiming to provide a benchmark for the future study of multi-focus image fusion. Finally, several major challenges remained in the current research of this field are discussed and some future prospects are put forward.

Category: Multi-focus Image Fusion

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