Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification

Through the continued development of technology, applying deep learning to remote sensing scene classification tasks is quite mature. The keys to effective deep learning model training are model architecture, training strategies, and image quality. From previous studies of the author using explainable artificial intelligence (XAI), image cases that have been incorrectly classified can be improved when the model has adequate capacity to correct the classification after manual image quality correction; however, the manual image quality correction process takes a significant amount of time. Therefore, this research integrates technologies such as noise reduction, sharpening, partial color area equalization, and color channel adjustment to evaluate a set of automated strategies for enhancing image quality. These methods can enhance details, light and shadow, color, and other image features, which are beneficial for extracting image features from the deep learning model to further improve the classification efficiency. In this study, we demonstrate that the proposed image quality enhancement strategy and deep learning techniques can effectively improve the scene classification performance of remote sensing images and outperform previous state-of-the-art approaches.

TWO-DIMENSIONAL COLOUR APPEARANCE SCALES FOR COLOUR STIMULI HAVING HIGH DYNAMIC RANGE

New colour appearance scales close to daily experience and image quality enhancement are highly desired including whiteness, blackness, vividness and depth. This article describes a new experiment to accumulate the data under HDR (high dynamic range) conditions. The data were then used to test the performance of different colour appearance scales such as CIELAB and CAM16-UCS plus the recent extension by Berns’ Vab*, Dab*. The results showed those Berns’ scales gave reasonable performance. However, there was no scale capable of predicting colour appearance data covering a wide dynamic range. New scales were developed based on the absolute scales of brightness and colourfulness of CAM16-UCS and gave accurate prediction to the data.

New colour appearance scales under high dynamic range conditions

New colour appearance scales close to daily experience and image quality enhancement are highly desired including whiteness, blackness, vividness and depth. This article describes a new experiment to accumulate the data under HDR (high dynamic range) conditions. The data were then used to test the performance of different colour appearance scales such as CIELAB and CAM16-UCS plus the recent extension by Berns' Vab*, Dab*. The results showed those Berns' scales gave a reasonable performance. However, it was found no scale is capable of predicting colour appearance data covering a wide dynamic range. New scales were developed based on the absolute scales of brightness and colourfulness of CAM16-UCS and gave accurate predictions to the data.

Current applications and development of artificial intelligence for digital dental radiography

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.