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Currently, various deep learning methods have been developed to address the image enhancement tasks based on paired high-quality images as references. For the low-light endoscopic image enhancement task, it is difficult to obtain paired high-quality images and to extract features from dark areas. In addition, the enhanced images easily appear color distortions. In this study, we propose an unsupervised deep learning scheme based on the Cycle Generative Adversarial Network to enhance the endoscopic image. Because extracting features in the dark areas is important but challenging, we embedded an adaptive reverse attention module in generators to help the network focus on low-light areas and enhance these areas. We also introduce a color consistency constraint to maintain color constancy. To evaluate the performance of the proposed enhancement method, a blind evaluation methodology is proposed in view of no specific quality assessment metric specially designed on this field. Extensive subjective and objective experiment results demonstrate that the proposed method is competent for the colorectal endoscopic image enhancement task, and performs better than both conventional methods and popular deep learning-based methods on 200 real-captured colonoscopy images. In the objective experiment, the proposed method ranks first with a PIQE score of 11.1525 and an NIQE score of 11.1525, outperforming five competing methods. It also receives the best results from an average score of 1.455 over 200 test images of the subjective experiment. [ABSTRACT FROM AUTHOR]

High dynamic range (HDR) displays based on liquid crystal panels require local dimming algorithms to reproduce content with high fidelity and HDR. However, most local dimming algorithms are developed by using hand-crafted features and most of them focus on low dynamic range images rather than HDR images. In addition, few local dimming algorithms can manage well the conflicting requirements of maintaining image quality while minimising power consumption. In this paper, we propose a genetic algorithm for backlight local dimming (GABLD) that generates images for the HDR display based on the given fitness function and the HDR content about to be displayed while considering the impact of power consumption and image quality. The effectiveness of the GABLD method is demonstrated in three aspects: the fidelity of reconstruction of HDR images, the power consumption, and the visualisation of HDR content on HDR displays. The results show that the proposed method has good performance in terms of both the image quality and power consumption, and it retains more details especially for areas with highlights when compared to traditional methods. [ABSTRACT FROM AUTHOR]

High dynamic range (HDR) displays with dual-panels are one type of displays that can provide HDR content. These are composed of a white backlight panel and a colour LCD panel. Local dimming algorithms are used to control the backlight panel in order to reproduce content with high dynamic range and contrast at a high fidelity. However, existing local dimming algorithms usually process low dynamic range (LDR) images, which are not suitable for processing HDR images. In addition, these methods use hand-crafted features to estimate the backlight values, which may not be suitable for many kind of images. In this work, a novel deep learning based local dimming method is proposed for rendering HDR images on dual-panel HDR displays. The method uses a Convolutional Neural Network (CNN) to directly predict backlight values, using as input the HDR image that is to be displayed. The model is designed and trained via a controllable power parameter that allows a user to trade off between power and quality. The proposed method is evaluated against seven other methods on a test set of 105 HDR images, using a variety of quantitative quality metrics. Results demonstrate improved display quality and better power consumption when using the proposed method compared to the best alternatives. [ABSTRACT FROM AUTHOR]