Equipment used in a crime is a clue to solve cases. Cars are often used in crimes for transportation. Security cameras are set in everywhere of downtowns. Drive recorders are set in many cars in Japan. Security cameras and drive recorders can record an image of a license number plate. However, the license number plate is often recorded in a small part of the image. It is needed to enlarge the image to read. However, the enlarged image is in low-resolution and blurry. Therefore, edges of the image are not clear. It is difficult to read the number plate from the low-resolution and blurry image. Machine learning has been used to identify blurry letters in an image. Performances of machine learning method change depending on classifiers. In this paper, performances of five classifiers for identifying the license number plate were compared. The proposed research can potentially contribute in solving criminal cases.
In the medical field, diagnosis based on images/videos is constantly becoming increasingly important. Recently, an 8K resolution endoscope has been developed. Lighting is necessary to obtain sufficiently lighted images/videos when endoscopic examinations or operations are performed. Although it is necessary to have sufficient illumination, the lighting condition of endoscopes is limited owing to the heat generated by the light source. Poor lighting conditions always produce noise. Particularly, the size of an 8K imaging cell is small, and the number of photons for it is limited. Noise is an important issue in 8K endoscopic images. During a diagnosis, in order to obtain detailed information on an ailing part, same areas are often monitored repeatedly. 8K endoscopic videos have a high resolution; having a high image quality in each frame of still images is important used during diagnoses. However, unlike videos, in still images, observers can easily distinguish not only resolution differences but also degradations, such as noise, in problematic areas. In this study, a total variation (TV) denoising method is used to reduce the noise of 8K endoscopic images.
Due to the advances of imaging devices technologies, high quality video has become easy to produce. There are still problems to be solved though. Gaussian noise is always mixed in images by photo-electric conversion. Noise reducer (NR) is a signal processing method to cope with the issue. However, noise level is required for NR to work effectively. Since signal processing methods for video should work in real-times, the noise level also should be detected in real-times as well. In this paper we propose a fast and accurate noise level detection algorithm.
The damage caused by the pirated films amounts to $1.3 billion a year. The pirated films are mainly created by re-shooting a screen in a theater or duplicating the official DVDs. Films are released in a theater. Before the official DVDs are released, 90% of the films are pirated in a theater and illegally made DVDs are sold. Although counter technologies are required, there is no effective proposals. The important requirement for the technology is that it does not degrade the image quality in a theater. It only degrades the re-shot video. In this paper, we report a novel method to make it possible.
With the advent of cellphone cameras, in particular, on smartphones, many people now take photos of themselves alone and with others in the frame; such photos are popularly known as “selfies”. Most smartphones are equipped with two cameras: the front-facing and rear cameras. The camera located on the back of the smartphone is referred to as the “out-camera,” whereas the one located on the front of the smartphone is called the “in-camera.” In-cameras are mainly used for selfies. Some smartphones feature high-resolution cameras. However, the original image quality cannot be obtained because smartphone cameras often have low-performance lenses. Super resolution (SR) is one of the recent technological advancements that has increased image resolution. We developed a new SR technology that can be processed on smartphones. Smartphones with new SR technology are currently available in the market have already registered sales. However, the effective use of new SR technology has not yet been verified. Comparing the image quality with and without SR on smartphone display is necessary to confirm the usefulness of this new technology. Methods that are based on objective and subjective assessments are required to quantitatively measure image quality. It is known that the typical object assessment value, such as Peak Signal to Noise Ratio (PSNR), does not go together with how we feel when we assess image/video. When digital broadcast started, the standard was determined using subjective assessment. Although subjective assessment usually comes at high cost because of personnel expenses for observers, the results are highly reproducible when they are conducted under right conditions and statistical analysis. In this study, the subjective assessment results for selfie images are reported.
Recently, security cameras and CCTV systems have become an important part of our daily lives. The rising demand for such systems has created business opportunities in this field, especially in big cities. Analogue CCTV systems are being replaced by digital systems, and HDTV CCTV has become quite common. HDTV CCTV can achieve images with high contrast and decent quality if they are clicked in daylight. However, the quality of an image clicked at night does not always have sufficient contrast and resolution because of poor lighting conditions. CCTV systems depend on infrared light at night to compensate for insufficient lighting conditions, thereby producing monochrome images and videos. However, these images and videos do not have high contrast and are blurred. We propose a nonlinear signal processing technique that significantly improves visual and image qualities (contrast and resolution) of low-contrast infrared images. The proposed method enables the use of infrared cameras for various purposes such as night shot and poor lighting environments under poor lighting conditions.