Multi-focus Image Fusion: A Benchmark
This provides a standardized platform for researchers in image processing to evaluate multi-focus image fusion methods, though it is incremental as it builds on existing algorithms and metrics.
The paper tackles the lack of fair and comprehensive performance comparison in multi-focus image fusion by introducing MFIFB, a benchmark with 105 image pairs, 30 algorithms, and 20 evaluation metrics, and uses it to identify effective algorithms and provide insights into the field.
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.