COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter

X-Ray Breast Images Denoising Method Based on the Convolutional Autoencoder

“…Te average SSIM values of literature [16] are improved by 0.01 compared to literature [14] and literature [15], and the SSIM of literature [17] is improved by 0.01 compared to literature [16]. Te SSIM of the proposed method in this paper is improved by 0.01 compared to literature [17].…”

“…All methods present visually well denoised results to some degree. When σ � 40, the PSNR of literature [16] and literature [15] are 0.98 dB and 0.12 dB greater than literature [14], respectively. Te PSNR of literature [17] is 0.54 dB higher than literature [16], while the PSNR of the proposed approach in this study is 0.04 dB higher than literature [17].…”

“…In this study, we conduct an experimental comparison of the proposed method's denoising performance with that of novel denoising methods. Tese methods include those mentioned in literatures [14], [15], [16], and [17]. In our experiments, we denoise the dataset constructed based on MIAS, and the experimental results are shown in Tables 1 and 2.…”

“…Although the detailed information of the image can be well recovered, the Gibbs efect will be produced after denoising, and the artifacts will be produced which cannot be eliminated. Guo et al proposed a median fltering method based on adaptive two-level threshold to solve the problems of low contrast and blurred boundary of traditional weighted median flters [15]. Tis method has a very good denoising efect on CT images of COVID-19, but it is not suitable for denoising mixed noise, which is easy to cause image blur and discontinuity.…”

Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion

“…Image features are the most basic attributes used to describe image content, and selecting reasonable features is the key for accurate GGO detection [35] , [36] . The selection of features is not unique, and the medical images generated by different types of GGO have different features [37] . GGO features are influenced by the comprehensiveness of its description and the accuracy of its characterization [38] .…”