During the COVID-19 pandemic, a robust system for masked face recognition has been required. Most existing solutions used many samples per identity for the model to recognize, but the processes involved are very laborious in a real-life scenario. Therefore, we propose “CPNet” as a suitable and reliable way of recognizing masked faces from only a few samples per identity. The prototype classifier uses a few-shot learning paradigm to perform the recognition process. To handle complex and occluded facial features, we incorporated the covariance structure of the classes to refine the class distance calculation. We also used sharpness-aware minimization (SAM) to improve the classifier. Extensive in-depth experiments on a variety of datasets show that our method achieves remarkable results with accuracy as high as 95.3%, which is 3.4% higher than that of the baseline prototype network used for comparison.

This paper reports on the relationships between the performance of retro-reflectors and the sharpness of an aerial image formed with aerial imaging by retro-reflection (AIRR). We have measured the retro-reflector divergence angle and evaluated aerial image sharpness by use of the contrast-transfer function. It is found that the divergence angle of the retro-reflected light is strongly related to the sharpness of the aerial image formed with AIRR.

Digital still cameras emerged following the introduction of the Sony Mavica analog prototype camera in 1981. These early cameras produced poor image quality and did not challenge film cameras for overall quality. By 1995 digital still cameras in expensive SLR formats had 6 mega-pixels and produced high quality images (with significant image processing). In 2005 significant improvement in image quality was apparent and lower prices for digital still cameras (DSCs) started a rapid decline in film usage and film camera sells. By 2010 film usage was mostly limited to professionals and the motion picture industry. The rise of DSCs was marked by a “pixel war” where the driving feature of the cameras was the pixel count where even moderate cost, ∼ $120, DSCs would have 14 mega-pixels. The improvement of CMOS technology pushed this trend of lower prices and higher pixel counts. Only the single lens reflex cameras had large sensors and large pixels. The drive for smaller pixels hurt the quality aspects of the final image (sharpness, noise, speed, and exposure latitude). Only today are camera manufactures starting to reverse their course and producing DSCs with larger sensors and pixels. This paper will explore why larger pixels and sensors are key to the future of DSCs.

This paper proposes a new method for automatic improvement in image quality through adjusting the image sharpness. This method does not need prior knowledge about image blur. To improve image quality, the sharpness must be adjusted to an optimal value. This paper shows a new method to evaluate sharpness without MTF. It is considered that the human visual system judges image sharpness mainly based upon edge area features. Therefore, attention is paid to the high spatial frequency components in the edge area. The value is defined by the average intensity of the high spatial fequency components in the edge area. This is called the image edge sharpness" value. Using several images, edge sharpness values are compared with experimental results for subjective sharpness. According to the experiments, the calculated edge sharpness values show a good linear relation with subjective sharpness. Subjective image sharpness does not have a monotonic relation with subjective image quality. If the edge sharpness value is in a particular range, the image quality is judged to be good. According to the subjective experiments, an optimal edge sharpness value for image quality was obtained. This paper also shows an algorithm to alter an image into one which has another edge sharpness value. By altering the image, which achieves optimal edge sharpness using this algorithm, image sharpness can be optimally adjusted automatically. This new image improving method was applied to several images obtained by scanning photographs. The experimental results were quite good.