Our proposed IQA was designed to consider MR-specific features and outperformed other no-reference IQAs designed mainly for photographic images. Magn Reson Med 80:914-924, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

No abstract

In modern digital photographs, most images have low dynamic range (LDR) formats, which means that the range of light intensities from the darkest to the brightest is much lower than the range that can be perceived by the human eye. Therefore, to visualize images as naturally as possible on devices that display them in high dynamic range (HDR) format, the LDR images need to be converted into HDR images. The aim of this study was to develop an adaptive inverse tone mapping operator (iTMO) that can convert a single LDR image into a realistic HDR image based on artificial neural networks. In contrast to conventional iTMO algorithms, our technique was developed by learning the complicated relationship between various LDR-HDR pair images, which enabled nearly ground-truth HDR images to be generated from various types of LDR images. The novel learning technique is called cumulative histogram-based learning and color difference learning. The superior performance of our technique over conventional methods was assessed through objective evaluations of various types of LDR and HDR images. INDEX TERMS High dynamic range, inverse tone mapping operator, cumulative histogram, color difference, convolutional neural network.

Image adjustment methods are one of the most widely used post-processing techniques for enhancing image quality and improving the visual preference of the human visual system (HVS). However, the assessment of the adjusted images has been mainly dependent on subjective evaluations. Also, most recently developed automatic assessment methods have mainly focused on evaluating distorted images degraded by compression or noise. The effects of the colorfulness, contrast, and sharpness adjustments on images have been overlooked. In this study, we propose a fully automatic assessment method that evaluates colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images while considering HVS preferences. The proposed method does not require a reference image and automatically calculates quantitative scores, visual preference, and quality assessment with respect to the level of colorfulness, contrast, and sharpness adjustment. The proposed method evaluates adjusted images based on the features extracted from high dynamic range images, which have higher colorfulness, contrast, and sharpness than that of low dynamic range images. Through experimentation, we demonstrate that our proposed method achieves a higher correlation with subjective evaluations than that of conventional assessment methods.