Article Preview
TopPresently, research in the field of image enhancement is continuously expanding. A study by Xu et al. (2020) proposed new insights into image processing for 360 degree video and images. Due to the spherical viewing range of 360 degree videos and images themselves, they had a larger data scale compared to ordinary videos and images. Therefore, compression and high-quality restoration of image data has become a key technology. The research analyzed the visual attention model and the spherical features of 360 degree images and looked forward to the future development of this technology (Xu et al., 2020). Ngugi et al. (2021) applied image processing technology to plant disease detection. The research combined machine learning technology with image processing technology, mainly using RGB images. The quality of CNN structural images was enhanced, which is the basis for the automatic recognition and formation of disease images. Lv et al. (2021) mainly used a CNN model with multiple branches to perform image enhancement on low light level images. This model combined attention mechanisms to form an end-to-end attention guidance strategy. At the same time, the form of dual attention map was adopted to achieve image enhancement and denoising. The first attention map could distinguish between underexposed and well-lit portions of the image, and the second attention map could distinguish between real texture and noise information. This method had strong fidelity.
Song et al. (2020) proposed an image enhancement model for underwater images. The model mainly depended on two optical parameters: background light and the transmission diagram. The model first performed a priori analysis of the dark channel of the underwater image and then used scene depth and reverse saturation to achieve color and contrast improvement of the image. At the same time, to balance the color and contrast of the image, white balance was used as the main post processing method. Its design method had excellent overall performance. Zhou et al. (2020) analyzed an image processing system that combines differentiators with traditional imaging systems. This image processing method could perform low-power and high-efficiency optical simulation, laying the foundation for further adaptive applications of computer vision processing and image enhancement processing technology.
On the other hand, as a relatively mature deep learning algorithm, the application of CNN in various fields is gradually deepening. Blanchet et al. (2020) proposed a stock price change prediction model combining CNN and Long Short-Term Memory (LSTM). This model could predict the closing price of stocks on the next day through the extracted characteristic data of stock price changes and could capture the closing price of stocks at different time points. This model had the ability to capture information characteristics over time, making it very suitable for predicting stock prices (Blanchet et al., 2020). Polsinelli et al. (2020) designed an automated diagnostic model for chest tomography images of COVID-19 based on light CNN. This model could effectively distinguish the chest CT images of COVID-19 from the healthy chest CT images. The model designed in this study could perform functions with more advantageous efficiency on notebook computers without hardware acceleration and had performance advantages (Polsinelli et al., 2020). Wang et al. (2020) designed an automated learning aid tool for CNN. This tool worked closely with CNN for users to use it more easily. Meanwhile, smooth transitions across abstraction levels were utilized to enhance the relevance between low-level mathematical computation and high-level model construction. This model could effectively assist users in algorithm learning.