Barriers Towards No-reference Metrics Application to Compressed Video Quality Analysis: on the Example of No-reference Metric NIQE

Antsiferova, Anastasia; Kulikov, Dmitriy; Kondranin, Denis; Vatolin, Dmitriy

doi:10.30987/graphicon-2019-2-22-27

Cited by 5 publications

References 8 publications

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4× Super‐resolution of unsupervised CT images based on GAN

Chen

et al. 2023

IET Image Processing

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Improving the resolution of computed tomography (CT) medical images can help doctors more accurately identify lesions, which is important in clinical diagnosis. In the absence of natural paired datasets of high resolution and low resolution image pairs, we abandoned the conventional Bicubic method and innovatively used a dataset of images of a single resolution to create near-natural high-low-resolution image pairs by designing a deep learning network and utilizing noise injection. In addition, we propose a super-resolution generative adversarial network called KerSRGAN which includes a super-resolution generator, super-resolution discriminator, and super-resolution feature extractor to achieve a 4× super-resolution of CT images. The results of an experimental evaluation show that KerSRGAN achieved superior performance compared to the state-of-the-art methods in terms of a quantitative comparison of non-reference image quality evaluation indicators on the generated 4× super-resolution CT images. Moreover, in terms of an intuitive visual comparison, the images generated by the KerSRGAN method had more precise details and better perceptual quality.

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4× Super‐resolution of unsupervised CT images based on GAN

Chen

et al. 2023

IET Image Processing

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Markerless Lung Tumor Localization From Intraoperative Stereo Color Fluoroscopic Images for Radiotherapy

Yan,

Fujii,

Shiinoki

et al. 2024

IEEE Access

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Accurately determining tumor regions from stereo fluoroscopic images during radiotherapy is a challenging task. As a result, high-density fiducial markers are implanted around tumors in clinical practice as internal surrogates of the tumor, which leads to associated surgical risks. This study was conducted to achieve lung tumor localization without the use of fiducial markers. We propose training a cascade U-net system to perform color to grayscale conversion, enhancement, bone suppression, and tumor detection to determine the precise tumor region. We generated Digitally Reconstructed Radiographs (DRRs) and tumor labels from 4D planning CT images as training data. An improved maximum projection algorithm and a novel color-to-gray conversion algorithm were proposed to improve the quality of the generated training data. Training a bone suppression model using bone-enhanced and bone-suppressed DRRs enables the bone suppression model to achieve better bone suppression performance. The mean peak signal-to-noise ratios in the test sets of the trained translation and bone suppression models are 39.284 ± 0.034 dB and 37.713 ± 0.724 dB, respectively. The results indicate that our proposed markerless tumor localization method is applicable in seven out of ten cases; in applicable cases, the centroid position error of the tumor detection model is less than 1.13 mm; and the calculated tumor center motion trajectories using the proposed network highly coincide with the motion trajectories of implanted fiducial markers in over 60% of captured groups, providing a promising direction for markerless tumor localization tracking methods.

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