In recent years, the incidence of diseases such as uterine fibroids in women in China has increased, which has raised people’s attention to the clinical effects and postoperative recovery effects of medical gynecological surgery. Regarding gynecological surgery, there are many types of myomectomy, and among them, vaginectomy is receiving widespread attention in gynecological surgery. This article studies the clinical effects of laparoscopic imaging technology-assisted vaginal resection and the functional status, mental status, and hormone levels of women during the recovery period, and analyzes the postoperative ovarian function and immune function of women. In this article, 111 patients with uterine fibroids were divided into the observation group and the reference group, of which 56 cases were in the reference group and 55 cases were in the observation group. The hospital stay, ovarian function status, immune status, uterine recovery, and tibia recovery of the control group and the experimental group were respectively analyzed. In the experiment, laparoscopic imaging technology-assisted vaginal resection surgery has no incision pain. It can be moved early on the ground and can be discharged within 2–4 days after surgery. This reduces medical expenses to a certain extent and can be performed at the same time without changing the position. This also performs other operations on the perineum or vagina, such as repairing the front and back walls of the vagina. Experimental results show that laparoscopic imaging technology-assisted vaginal resection can reduce pain and speed up the recovery of ovarian function. Three months after surgery, 59.6% of patients resumed ovulation, much higher than 42.3% in the control group. Vaginectomy using laparoscopic imaging technology can improve immunity by adjusting hormone levels in the body in a short time.

The Natural Scene derived Spatial Frequency Response (NS-SFR) is a novel camera system performance measure that derives SFRs directly from images of natural scenes and processes them using ISO12233 edge-based SFR (e-SFR) algorithm. NS-SFR is a function of both camera system performance and scene content. It is measured directly from captured scenes, thus eliminating the use of test charts and strict laboratory conditions. The effective system e-SFR can be subsequently estimated from NS-SFRs using statistical analysis and a diverse dataset of scenes. This paper first presents the NS-SFR measuring framework, which locates, isolates, and verifies suitable step-edges from captures of natural scenes. It then details a process for identifying the most likely NS-SFRs for deriving the camera system e-SFR. The resulting estimates are comparable to standard e-SFRs derived from test chart inputs, making the proposed method a viable alternative to the ISO technique, with potential for real-time camera system performance measurements.

We present the results of our image analysis of portrait art from the Roman Empire’s Julio-Claudian dynastic period. Our novel approach involves processing pictures of ancient statues, cameos, altar friezes, bas-reliefs, frescoes, and coins using modern mobile apps, such as Reface and FaceApp, to improve identification of the historical subjects depicted. In particular, we have discovered that the Reface app has limited, but useful capability to restore the approximate appearance of damaged noses of the statues. We confirm many traditional identifications, propose a few identification corrections for items located in museums and private collections around the world, and discuss the advantages and limitations of our approach. For example, Reface may make aquiline noses appear wider or shorter than they should be. This deficiency can be partially corrected if multiple views are available. We demonstrate that our approach can be extended to analyze portraiture from other cultures and historical periods. The article is intended for a broad section of the readers interested in how the modern AI-based solutions for mobile imaging merge with humanities to help improve our understanding of the modern civilization’s ancient past and increase appreciation of our diverse cultural heritage.

Many image reproduction devices, such as printers, are limited to only a few numbers of printing inks. Halftoning, which is the process to convert a continuous-tone image into a binary one, is, therefore, an essential part of printing. An iterative halftoning method, called Iterative Halftoning Method Controlling the Dot Placement (IMCDP), which has already been studied by research scholars, generally results in halftones of good quality. In this paper, we propose a structure-based alternative to this algorithm that improves the halftone image quality in terms of sharpness, structural similarity, and tone preservation. By employing appropriate symmetrical and non-symmetrical Gaussian filters inside the proposed halftoning method, it is possible to adaptively change the degree of sharpening in different parts of the continuous-tone image. This is done by identifying a dominant line in the neighborhood of each pixel in the original image, utilizing the Hough Transform, and aligning the dots along the dominant line. The objective and subjective quality assessments verify that the proposed structure-based method not only results in sharper halftones, giving more three-dimensional impression, but also improves the structural similarity and tone preservation. The adaptive nature of the proposed halftoning method makes it an appropriate algorithm to be further developed to a 3D halftoning method, which could be adapted to different parts of a 3D object by exploiting both the structure of the images being mapped and the 3D geometrical structure of the underlying printed surface.

The Natural Scene derived Spatial Frequency Response (NS-SFR) framework automatically extracts suitable step-edges from natural pictorial scenes and processes these edges via the edge-based ISO12233 (e-SFR) algorithm. Previously, a novel methodology was presented to estimate the standard e-SFR from NS-SFR data. This paper implements this method using diverse natural scene image datasets from three characterized camera systems. Quantitative analysis was carried out on the system e-SFR estimates to validate accuracy of the method. Both linear and non-linear camera systems were evaluated. To investigate how scene content and dataset size affect system e-SFR estimates, analysis was conducted on entire datasets, as well as subsets of various sizes and scene group types. Results demonstrate that system e-SFR estimates strongly correlate with results from test chart inputs, with accuracy comparable to that of the ISO12233. Further work toward improving and fine-tuning the proposed methodology for practical implementation is discussed.

With the proliferation of smartphones and social networking services, the opportunities for individuals to take photographs have increased exponentially. In a previous study, the perceived gloss of an object was reduced by its representing as a digital image and compared with a real object. It is also known that image editing, such as lossy image compression, can reduce the glossiness of an image. Therefore, the glossiness of real objects may be easily changed in digital images; thus, a method for appropriately editing the gloss in digital images is required for post-processing. In this study, we propose a gloss appearance editing method for various material objects in a single digital image. The proposed method consists of three steps: color space conversion, gloss detection, and gloss editing. The relationship between the proposed method and the respective reflection models of inhomogeneous objects, metallic objects, and translucent objects was analyzed. Consequently, we determined that the gloss editing of the proposed method is equivalent to editing the specular reflection component of an inhomogeneous object, the grazing reflection component of a metallic object, and the specular reflection component of a translucent object. We applied the proposed method to test images including objects of various materials and confirmed its effectiveness through a subjective evaluation by visual inspection and an objective evaluation using image statistics.

In recent years, smartphone-based colour imaging systems are being increasingly used for Neonatal jaundice detection applications. These systems are based on the estimation of bilirubin concentration levels that correlates with newborns’ skin colour images corresponding to total serum bilirubin (TSB) and transcutaneous bilirubinometry (TcB) measurements. However, the colour reproduction capacity of smartphone cameras are known to be influenced by various factors including the technological and acquisition process variabilities. To make an accurate bilirubin estimation, irrespective of the type of smartphone and illumination conditions used to capture the newborns’ skin images, an inclusive and complete model, or data set, which can represent all the possible real world acquisitions scenarios needs to be utilized. Due to various challenges in generating such a model or a data set, some solutions tend towards the application of reduced data set (designed for reference conditions and devices only) and colour correction systems (for the transformation of other smartphone skin images to the reference space). Such approaches will make the bilirubin estimation methods highly dependent on the accuracy of their employed colour correction systems, and the capability of reducing device-to-device colour reproduction variability. However, the state-of-the-art methods with similar methodologies were only evaluated and validated on a single smartphone camera. The vulnerability of the systems in making an incorrect jaundice diagnosis can only be shown with a thorough investigation of the colour reproduction variability for extended number of smartphones and illumination conditions. Accordingly, this work presents and discuss the results of such broad investigation, including the evaluation of seven smartphone cameras, ten light sources, and three different colour correction approaches. The overall results show statistically significant colour differences among devices, even after colour correction applications, and that further analysis on clinically significance of such differences is required for skin colour based jaundice diagnosis.

In recent years, deep neural networks (DNNs) have accomplished impressive success in various applications, including autonomous driving perception tasks. However, current deep neural networks are easily deceived by adversarial attacks. This vulnerability raises significant concerns, particularly in safety-critical applications. As a result, research into attacking and defending DNNs has gained much coverage. In this work, detailed adversarial attacks are applied on a diverse multi-task visual perception deep network across distance estimation, semantic segmentation, motion detection, and object detection. The experiments consider both white and black box attacks for targeted and un-targeted cases, while attacking a task and inspecting the effect on all others, in addition to inspecting the effect of applying a simple defense method. We conclude this paper by comparing and discussing the experimental results, proposing insights and future work. The visualizations of the attacks are available at https://youtu.be/6AixN90budY.

Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics utilized and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics utilized, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model ‘goodness of fit’ to data and model ‘complexity’. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.