During digital microscopy of an oil painting surface it is inconvenient to analyze an entire image due to multiple defocused areas. The defocusing is usually caused by the small depth of the lens and the rough surface curve. Thus, these microscopic images in an oil painting have multiple focal points, which indicates multi-focus images. We present a multi-focus fusion synthesizing a focused image from scans based on focal direction and selection of focused places. Based on microscopic characteristics, a common scanned area of the images was defined to unify the lens multiplication. A focus index was applied to each pixel to identify well-focused pixels and generate a mapping image in the focal direction. Subsequently, a median filter was applied to the mapping image and a multifocal image was acquired based on actual pixel values obtained from the mapping image. The proposed method was utilized in analyzing oil painting samples carrying rough surface curves. The multifocal image facilitated the analysis of the oil painting surface and resulted in enhanced quality compared with other methods. The proposed method can be used to generate useful images in scientific and industrial microscopy.

As the size of semiconductor devices gradually decreases, it is important to measure and analyze semiconductor devices, to improve the image quality of semiconductors. We use VDSR, one of the Super-Resolution methods to improve the quality of semiconductor devices’ SEM images. VDSR is also a convolutional neural network that can be optimized with various parameters. In this study, a VDSR model for semiconductor devices’ SEM images was optimized using parameters such as depth of layers and amount of training data. Meanwhile, the quantitative evaluation and the qualitative evaluation did not match at the low scale factor. To solve this problem, we proposed an MTF measurement method using the slanted edge for better quantitative evaluation. This method was verified by comparing the results with the PSNR and SSIM index results, which are known as quality indicators. Based on the results, it was confirmed that using the MTF value could be a better approach for the evaluation of SEM images of the semiconductor device than using PSNR and SSIM.