A deep evaluator for image retargeting quality by geometrical and contextual interaction

An image is compressed or stretched during the multidevice displaying, which will have a very big impact on perception quality. In order to solve this problem, a variety of image retargeting methods have been proposed for the retargeting process. However, how to evaluate the results of different image retargeting is a very critical issue. In various application systems, the subjective evaluation method cannot be applied on a large scale. So we put this problem in the accurate objective-quality evaluation. Currently, most of the image retargeting quality assessment algorithms use simple regression methods as the last step to obtain the evaluation result, which are not corresponding with the perception simulation in the human vision system (HVS). In this paper, a deep quality evaluator for image retargeting based on the segmented stacked AutoEnCoder (SAE) is proposed. Through the help of regularization, the designed deep learning framework can solve the overfitting problem. The main contributions in this framework are to simulate the perception of retargeted images in HVS. Especially, it trains two separated SAE models based on geometrical shape and content matching. Then, the weighting schemes can be used to combine the obtained scores from two models. Experimental results in three well-known databases show that our method can achieve better performance than traditional methods in evaluating different image retargeting results

Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks

Numerous image superresolution (SR) algorithms have been proposed for reconstructing high-resolution (HR) images from input images with lower spatial resolutions. However, effectively evaluating the perceptual quality of SR images remains a challenging research problem. In this paper, we propose a no-reference/blind deep neural network-based SR image quality assessor (DeepSRQ). To learn more discriminative feature representations of various distorted SR images, the proposed DeepSRQ is a two-stream convolutional network including two subcomponents for distorted structure and texture SR images. Different from traditional image distortions, the artifacts of SR images cause both image structure and texture quality degradation. Therefore, we choose the two-stream scheme that captures different properties of SR inputs instead of directly learning features from one image stream. Considering the human visual system (HVS) characteristics, the structure stream focuses on extracting features in structural degradations, while the texture stream focuses on the change in textural distributions. In addition, to augment the training data and ensure the category balance, we propose a stride-based adaptive cropping approach for further improvement. Experimental results on three publicly available SR image quality databases demonstrate the effectiveness and generalization ability of our proposed DeepSRQ method compared with state-of-the-art image quality assessment algorithms

Multi feature-rich synthetic colour to improve human visual perception of point clouds

Although point features have shown their usefulness in classification with Machine Learning, point cloud visualization enhancement methods focus mainly on lighting. The visualization of point features helps to improve the perception of the 3D environment. This paper proposes Multi Feature-Rich Synthetic Colour (MFRSC) as an alternative non-photorealistic colour approach of natural-coloured point clouds. The method is based on the selection of nine features (reflectance, return number, inclination, depth, height, point density, linearity, planarity, and scattering) associated with five human perception descriptors (edges, texture, shape, size, depth, orientation). The features are reduced to fit the RGB display channels. All feature permutations are analysed according to colour distance with the natural-coloured point cloud and Image Quality Assessment. As a result, the selected feature permutations allow a clear visualization of the scene's rendering objects, highlighting edges, planes, and volumetric objects. MFRSC effectively replaces natural colour, even with less distorted visualization according to BRISQUE, NIQUE and PIQE. In addition, the assignment of features in RGB channels enables the use of MFRSC in software that does not support colorization based on point attributes (most commercially available software). MFRSC can be combined with other non-photorealistic techniques such as Eye-Dome Lighting or Ambient Occlusion.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431F 2022/08Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Universidade de Vigo/CISU

Endoscopic Vision Augmentation Using Multiscale Bilateral-Weighted Retinex for Robotic Surgery

医疗机器人手术视觉是微创外科手术成功与否的关键所在。由于手术器械医学电子内镜自身内在的局限性，导致了手术视野不清晰、光照不均、多烟雾等诸多问题，使得外科医生无法准确快速感知与识别人体内部器官中的神经血管以及病灶位置等结构信息，这无疑增加了手术风险和手术时间。针对这些手术视觉问题，本论文提出了一种基于双边滤波权重分析的多尺度Retinex模型方法，对达芬奇医疗机器人手术过程中所采集到的病患视频进行处理与分析。经过外科医生对实验结果的主观评价，一致认为该方法能够大幅度地增强手术视野质量；同时客观评价实验结果表明本论文所提出方法优于目前计算机视觉领域内的图像增强与恢复方法。 厦门大学信息科学与技术学院计算机科学系罗雄彪教授为本文第一作者。【Abstract】Endoscopic vision plays a significant role in minimally invasive surgical procedures. The visibility and maintenance of such direct in-situ vision is paramount not only for safety by preventing inadvertent injury, but also to improve precision and reduce operating time. Unfortunately, endoscopic vision is unavoidably degraded due to illumination variations during surgery. This work aims to restore or augment such degraded visualization and quantitatively evaluate it during robotic surgery. A multiscale bilateral-weighted retinex method is proposed to remove non-uniform and highly directional illumination and enhance surgical vision, while an objective noreference image visibility assessment method is defined in terms of sharpness, naturalness, and contrast, to quantitatively and objectively evaluate endoscopic visualization on surgical video sequences. The methods were validated on surgical data, with the experimental results showing that our method outperforms existent retinex approaches. In particular, the combined visibility was improved from 0.81 to 1.06, while three surgeons generally agreed that the results were restored with much better visibility.The authors thank the assistance of Dr. Stephen Pautler for facilitating the data acquisition, Dr. A. Jonathan McLeod and Dr.Uditha Jayarathne for helpful discussions