In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g., visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images, which are generally thought to be of the best quality. In this paper, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measure of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image data sets. The results of experiments on nine data sets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-reference, reduced-reference and NA IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images, and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications.

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New challenges have been brought out along with the emerging of 3D-related technologies, such as virtual reality, augmented reality (AR), and mixed reality. Free viewpoint video (FVV), due to its applications in remote surveillance, remote education, and so on, based on the flexible selection of direction and viewpoint, has been perceived as the development direction of next-generation video technologies and has drawn a wide range of researchers' attention. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a reliable real-time blind quality evaluation and monitoring system is urgently required. But existing assessment metrics do not render human judgments faithfully mainly because geometric distortions are generated by DIBR. To this end, this paper proposes a novel referenceless quality metric of DIBR-synthesized images using the autoregression (AR)-based local image description. It was found that, after the AR prediction, the reconstructed error between a DIBR-synthesized image and its AR-predicted image can accurately capture the geometry distortion. The visual saliency is then leveraged to modify the proposed blind quality metric to a sizable margin. Experiments validate the superiority of our no-reference quality method as compared with prevailing full-, reduced-, and no-reference models.

The glymphatic system functions in the removal of potentially harmful metabolites and proteins from the brain. Dynamic, contrast-enhanced MRI was used in fully awake rats to follow the redistribution of intraventricular contrast agent entrained to the light–dark cycle and its hypothetical relationship to the sleep–waking cycle, blood flow, and brain temperature in specific brain areas. Brain areas involved in circadian timing and sleep–wake rhythms showed the lowest redistribution of contrast agent during the light phase or time of inactivity and sleep in rats. Global brain redistribution of contrast agent was heterogeneous. The redistribution was highest along the dorsal cerebrum and lowest in the midbrain/pons and along the ventral surface of the brain. This heterogeneous redistribution of contrast agent paralleled the gradients and regional variations in brain temperatures reported in the literature for awake animals. Three-dimensional quantitative ultrashort time-to-echo contrast-enhanced imaging was used to reconstruct small, medium, and large arteries and veins in the rat brain and revealed areas of lowest redistribution overlapped with this macrovasculature. This study raises new questions and theoretical considerations of the impact of the light–dark cycle, brain temperature, and blood flow on the function of the glymphatic system.

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