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Noises are commonly present in grayscale images, particularly in Scanning Electron Microscope (SEM) images especially secondary emission noise, which can significantly affect their quality. Signal-to-Noise Ratio (SNR) estimation techniques are crucial for assessing the quality of these images by quantifying the ratio of signal strength to background noise. Traditionally, SNR estimation required two microscope images of identical sample areas, which made it difficult to determine the SNR of existing micrographs or stored images and resulted in a time-consuming process due to the necessity for precise alignment. To address these challenges, innovators have developed methods to assess an image’s SNR value using only a single image, thereby eliminating the alignment problem and speeding up the process. The single image SNR estimation can be used in various fields such as semiconductor and biological structure analysis. Additionally, SNR estimation methods can be used to accurately estimate SNR values, remove noise from images, identify IC cracks, avoid sample contamination, and reduce the charging effect. This review paper explains and analyzes the working principles of SEM, the various types of noise present in SEM images, and the SNR estimation techniques used to mitigate these issues. We cover both traditional two-image methods and modern single-image methods, discussing their respective advantages and limitations. By providing a comprehensive overview of these techniques, this paper aims to highlight the importance of accurate SNR estimation in improving SEM image quality and to present the latest advancements that make SNR estimation more efficient and applicable to a broader range of SEM imaging scenarios.

Objectives: To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low‐dose chest CT in comparison with 40% adaptive statistical iterative reconstruction‐Veo (ASiR‐V40%) algorithm. Methods: This retrospective study included 86 patients who underwent low‐dose CT for lung cancer screening. Images were reconstructed with ASiR‐V40% and DLIR at low (DLIR‐L), medium (DLIR‐M), and high (DLIR‐H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures. Results: The effective dose was 1.03 ± 0.36 mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5 HU among the four reconstructions. Compared with ASiR‐V40%, the DLIR‐L, DLIR‐M, and DLIR‐H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p < 0.001). ASiR‐V40%, DLIR‐L, and DLIR‐M had equivalent score in visualizing small structures (all p > 0.05), whereas DLIR‐H had slightly lower score. Conclusions: Compared with ASiR‐V40%, DLIR significantly reduces image noise in low‐dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application. [ABSTRACT FROM AUTHOR]

Edge detection is very important technique to reveal significant areas in the digital image, which could aids the feature extraction techniques. In fact it is possible to remove un-necessary parts from image, using edge detection. A lot of edge detection techniques has been made already, but we propose a robust evolutionary based system to extract the vital parts of the image. System is based on a lot of pre and post-processing techniques such as filters and morphological operations, and applying modified Ant Colony Optimization edge detection method to the image. The main goal is to test the system on different color spaces, and calculate the system’s performance. Another novel aspect of the research is using depth images along with color ones, which depth data is acquired by Kinect V.2 in validation part, to understand edge detection concept better in depth data. System is going to be tested with 10 benchmark test images for color and 5 images for depth format, and validate using 7 Image Quality Assessment factors such as Peak Signal-to-Noise Ratio, Mean Squared Error, Structural Similarity and more (mostly related to edges) for prove, in different color spaces and compared with other famous edge detection methods in same condition. Also for evaluating the robustness of the system, some types of noises such as Gaussian, Salt and pepper, Poisson and Speckle are added to images, to shows proposed system power in any condition. The goal is reaching to best edges possible and to do this, more computation is needed, which increases run time computation just a bit more. But with today’s systems this time is decreased to minimum, which is worth it to make such a system. Acquired results are so promising and satisfactory in compare with other methods available in validation section of the paper.