Enhancing Focus Volume through Perceptual Focus Factor in Shape-from-Focus

Khurram Ashfaq and Muhammad Tariq Mahmood ()
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Khurram Ashfaq: Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of Korea
Muhammad Tariq Mahmood: Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of Korea

Mathematics, 2023, vol. 12, issue 1, 1-16

Abstract: Shape From Focus (SFF) reconstructs a scene’s shape using a series of images with varied focus settings. However, the effectiveness of SFF largely depends on the Focus Measure (FM) used, which is prone to noise-induced inaccuracies in focus values. To address these issues, we introduce a perception-influenced factor to refine the traditional Focus Volume (FV) derived from a traditional FM. Owing to the strong relationship between the Difference of Gaussians (DoG) and how the visual system perceives edges in a scene, we apply it to local areas of the image sequence by segmenting the image sequence into non-overlapping blocks. This process yields a new metric, the Perceptual Focus Factor (PFF), which we combine with the traditional FV to obtain an enhanced FV and, ultimately, an enhanced depth map. Intensive experiments are conducted by using fourteen synthetic and six real-world data sets. The performance of the proposed method is evaluated using quantitative measures, such as Root Mean Square Error (RMSE) and correlation. For fourteen synthetic data sets, the average RMSE measure of 6.88 and correction measure of 0.65 are obtained, which are improved through PFF from an RMSE of 7.44 and correlation of 0.56, respectively. Experimental results and comparative analysis demonstrate that the proposed approach outperforms the traditional state-of-the-art FMs in extracting depth maps.

Keywords: shape from focus; focus measure; directional ring difference filter; perceptual focus factor; depth map (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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