Mandibular fracture is one of the most frequent injuries in oral and maxillo-facial surgery. Radiologists diagnose mandibular fractures using panoramic radiography and cone-beam computed tomography (CBCT). Panoramic radiography is a conventional imaging modality, which is less complicated than CBCT. This paper proposes the diagnosis method of mandibular fractures in a panoramic radiograph based on a deep learning system without the intervention of radiologists. The deep learning system used has a one-stage detection called you only look once (YOLO). To improve detection accuracy, panoramic radiographs as input images are augmented using gamma modulation, multi-bounding boxes, single-scale luminance adaptation transform, and multi-scale luminance adaptation transform methods. Our results showed better detection performance than the conventional method using YOLO-based deep learning. Hence, it will be helpful for radiologists to double-check the diagnosis of mandibular fractures.

This paper proposes a deep learning algorithm that can improve pill identification performance using limited training data. In general, when individual pills are detected in multiple pill images, the algorithm uses multiple pill images from the learning stage. However, when there is an increase in the number of pill types to be identified, the pill combinations in an image increase exponentially. To detect individual pills in an image that contains multiple pills, we first propose an effective database expansion method for a single pill. Then, the expanded training data are used to improve the detection performance. Our proposed method shows higher performance improvement than the existing algorithms despite the limited imaging and data set size. Our proposed method will help minimize problems, such as loss of productivity and human error, which occur while inspecting dispensed pills.

Image fusion combines images with different information to create a single, information-rich image. The process may either involve synthesizing images using multiple exposures of the same scene, such as exposure fusion, or synthesizing images of different wavelength bands, such as visible and near-infrared (NIR) image fusion. NIR images are frequently used in surveillance systems because they are beyond the narrow perceptual range of human vision. In this paper, we propose an infrared image fusion method that combines high and low intensities for use in surveillance systems under low-light conditions. The proposed method utilizes a depth-weighted radiance map based on intensities and details to enhance local contrast and reduce noise and color distortion. The proposed method involves luminance blending, local tone mapping, and color scaling and correction. Each of these stages is processed in the LAB color space to preserve the color attributes of a visible image. The results confirm that the proposed method outperforms conventional methods.

Mandibular fractures are the most common fractures in dentistry. Since diagnosing a mandibular fracture is difficult when only panoramic radiographic images are used, most doctors use cone beam computed tomography (CBCT) to identify the patient’s fracture location. In this study, considering the diagnosis of mandibular fractures using the combined deep learning technique, YOLO and U-Net were used as auxiliary diagnostic methods to detect the location of mandibular fractures based on panoramic images without CBCT. In a previous study, mandibular fracture diagnosis was performed using YOLO learning; in the detection performance result of the YOLOv4-based mandibular fracture diagnosis module, the precision score was approximately 97%, indicating that there was almost no misdiagnosis. In particular, fractures in the symphysis, body, angle, and ramus tend to be distributed in the middle of the mandible. Owing to the irregular fracture types and overlapping location information, the recall score was approximately 79%, which increased the detection of undiagnosed fractures. In many cases, fractures that are clearly visible to the human eye cannot be grasped. To overcome these shortcomings, the number of undiagnosed fractures can be reduced using a combination of the U-Net and YOLOv4 learning modules. U-Net is advantageous for the segmentation of fractures spread over a wide area because it performs semantic segmentation. Consequently, the undiagnosed case in the middle of the mandible, where YOLO was weak, was somewhat supplemented by the U-Net module. The precision score of the combined module was 95%, similar to that of the previous method, and the recall score improved to 87%, as the number of undiagnosed cases was reduced. Through this study, the performance of a deep learning method that can be used for the diagnosis of the mandibular bone has been improved, and it is anticipated that as an auxiliary diagnostic inspection device, it will assist dentists in making diagnoses.

Numerous studies are underway to enhance the identification of surroundings in nighttime environments. These studies explore methods such as utilizing infrared images to improve night image visibility or converting night images into day-like representations for enhanced visibility. This research presents a technique focused on converting the road conditions depicted in night images to resemble daytime scenes. To facilitate this, a paired dataset is created by augmenting limited day and night image data using CycleGAN. The model is trained using both original night images and single-scale luminance transform (SLAT) day images to enhance the level of detail in the converted daytime images. However, the generated daytime images may exhibit sharpness and noise issues. To address these concerns, an image processing approach, inspired by the Stevens effect and local blurring, which align with visual characteristics, is employed to reduce noise and enhance image details. Consequently, this study contributes to improving the visibility of night images by means of day image conversion and subsequent image processing. The proposed night-to-day image translation in this study has a processing time of 0.81 s, including image processing, which is less than one second. Therefore, it is considered valuable as a module for daytime image translation. Additionally, the image quality assessment metric, BRISQUE, yielded a score of 19.8, indicating better performance compared to conventional methods. The outcomes of this research hold potential applications in fields such as CCTV surveillance systems and self-driving cars.

Tone-mapping algorithms aim to compress a wide dynamic range image into a narrower dynamic range image suitable for display on imaging devices. A representative tone-mapping algorithm, Retinex theory, reflects color constancy based on the human visual system and performs dynamic range compression. However, it may induce halo artifacts in some areas or degrade chroma and detail. Thus, this paper proposes a Retinex jointed multiscale contrast limited adaptive histogram equalization method. The proposed algorithm reduces localized halo artifacts and detail loss while maintaining the tone-compression effect via high-scale Retinex processing. A performance comparison of the experimental results between the proposed and existing methods confirms that the proposed method effectively reduces the existing problems and displays better image quality.