Development of Image Pre-Processing Methods for Software Compensation of Anomal Refraction of the Observer’s Eyes

Underwater image target detection is an important part of exploring the ocean. This paper adopts cascade classifier and image preprocessing method. Firstly, it selects candidate regions on a given picture, then extracts feature from them and finally uses the trained classifier to detect. It focuses on the self-defined training of the cascade classifier, and trains the cascade classifier by collecting a large number of underwater target images. Secondly, it uses some of image preprocessing to make the detection effect more accurate. Finally, the simulation results show that it can achieve the target detection of underwater image by using the method of self-defined cascade classifier and image preprocessing.

Purpose The purpose of this study was to use the neural network to distinguish optic edema (ODE), and optic atrophy from normal fundus images and try to use visualization to explain the artificial intelligence methods. Methods Three hundred and sixty-seven images of ODE, 206 images of optic atrophy, and 231 images of normal fundus were used, which were provided by two hospitals. A set of image preprocessing and data enhancement methods were created and a variety of different neural network models, such as VGG16, VGG19, Inception V3, and 50-layer Deep Residual Learning (ResNet50) were used. The accuracy, recall, F1-score, and ROC curve under different networks were analyzed to evaluate the performance of models. Besides, CAM (class activation mapping) was utilized to find the focus of neural network and visualization of neural network with feature fusion. Results Our image preprocessing and data enhancement method significantly improved the accuracy of model performance by about 10%. Among the networks, VGG16 had the best effect, as the accuracy of ODE, optic atrophy and normal fundus were 98, 90, and 95%, respectively. The macro-average and micro-average of VGG16 both reached 0.98. From CAM we can clearly find out that the focus area of the network is near the optic cup. From feature fusion images, we can find out the difference between the three types fundus images. Conclusion Through image preprocessing, data enhancement, and neural network training, we applied artificial intelligence to identify ophthalmic diseases, acquired the focus area through CAM, and identified the difference between the three ophthalmic diseases through neural network middle layers visualization. With the help of assistant diagnosis, ophthalmologists can evaluate cases more precisely and more clearly.

For optimal computer vision outcomes, attention to image pre-processing is required so that one can improve image features by eliminating unwanted falsification. This book emphasizes various image pre-processing methods which are necessary for early extraction of features from the image. Effective use of image pre-processing can offer advantages and resolve complications that finally results in improved detection of local and global features. Different approaches for image enrichments and improvements are conferred in this book that will affect the feature analysis depending on how the procedures are employed. Key Features Describes the methods used to prepare images for further analysis which includes noise removal, enhancement, segmentation, local, and global feature description Includes image data pre-processing for neural networks and deep learning Covers geometric, pixel brightness, filtering, mathematical morphology transformation, and segmentation pre-processing techniques Illustrates a combination of basic and advanced pre-processing techniques essential to computer vision pipeline Details complications to resolve using image pre-processing

Comparison of Inter-Observer Variability and Diagnostic Performance of the Fifth Edition of BI-RADS for Breast Ultrasound of Static versus Video Images

In this chapter, various image pre-processing methods, image enhancement, performance metrics for image enhancement and image segmentation techniques are discussed. Further, an algorithm is designed for face detection in MATLAB 2018a. It can be concluded that image pre-processing is an important task before feeding images to any intelligent system. Pre-processing mainly involves enhancement techniques that are used for removing noise that may occur due to blurriness, night view, dim light, occlusion, shadows, etc. Therefore, when we design a system for surveillance or detection of objects in a video or any other image recognition task, the first step is image pre-processing so that high performance of the system can be achieved.

Collisions of aircraft and birds cause serious flight accidents, and various studies are underway to find a solution to the problem. In recent image recognition studies, state-of-the-art deep learning technologies have been actively applied. This paper proposes image preprocessing and bird detection methods in all dynamic environments using Convolutional Neural Network (CNN) technology. Image preprocessing separates moving creatures from the dynamic background and removes the background. When image preprocessing is complete, the image of the moving object remaining in the frame is used as input data for the learning model to determine whether the bird is in the frame. We used the Inception -v3 neural network model to improve the accuracy of small object classifications.

Natural image statistics are well known to have a spatial frequency power spectra that has a 1/f^a behavior, with a typically stated as between 2 and 4. This indicates an invariance to scale. Further work has theorized how the visual system is tuned for such statistics in visual cortex (V1) [1]. Color image statistics also show an invariance to scale [2]. The luminance histogram is typically understood to be log normal with respect to luminance, although for HDR images, a subcomponent with skew toward much higher luminances is observed. Color statistics were initially described at the simplest level via the gray world hypothesis [3], but more details are now available, even at the hyperspectral [4]. The a power function for HDR was found to increase from the lower values of 2 to more typical values of 4 and 5 [5]. For temporal statistics, the data tends to be measured primarily for media, with a 1/f^a for scene cut statistics [6], and temporal frequency and temporal frequency for media with a focus on the motion statistics via optical flow [7]. Statistics for purely natural as well as human made environments (e.g., buildings and the resulting perspective geometry) have been studied, each having different orientation statistics [8]. The use of image statistics for standardized assessment of television power consumption was used to replace test targets, which were often detected and used to lower TV power consumption in well known cheating schemes. To prevent this, a short test video that had luminance statistics matching 48 hours of broadcast content was generated and used for TV power testing [9]. The highly adaptative nature of current TVs (power limiting, dual modulation, dynamic response) has motivated researchers to incorporate complex noise fields following natural image statistics into measurement targets [10,11]. One particular natural image statistic-based still image test target (dead leaves) is widely used in camera optics and sensor development. Algorithm development and testing for image and video processing has almost always been ad hoc, with a mixture of geometric test targets and hand selected test images, sometimes aiming to be corner cases, sometimes not. More recently, large data sets of images have been used to train various neural network models for tasks such as super resolution, bit rate compression, and dynamic range mapping. However, images are not ergodic, and possibly not even wide-sense stationary. We propose the use of imagery based on noise following the natural image statistics for spatio-chromatic (and temporal) to compactly probe the wide variety of image possibilities for algorithmic development, in addition to the existing uses for image capture and display analysis. While we don’t suggest replacing actual practical imagery, we believe such noise fields can augment image algorithm analysis. To address the problem of non-ergodicity, we allow the basic power term a in the natural image statistic model to vary over a large range in a video, such that it includes the extremes of white noise and low frequency gradients. We use color image statistic models that include decorrelated colors to generate the RGB video. We will present results for traditional adaptive data compression (with chromatic subsampling), as well as a more contemporary neural network approach (Neural Fields [12]) as applied to upscaling and denoising. We analyze the results both visually and through several recent color image quality models. Field DJ. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A, 1987; 4:2379-2394 C. Parraga, T. Troscianko, and D.J. Tolhurst (2002) spatiochromatic properties of natural images and human vision. Current Biology V 12 R. M. Evans, Method for correcting photographic color prints, US Patent 2,571,697 (1951) A. Chakrabarti and T. Zickler (2011) Statistics of real-world hyperspectral images CVPR R. Dror, A. Willsky, and E. Adelson (2004) statistical characterization of real-world illumination. JOV V4 J. Cutting (2019) Sequences in popular cinema generate inconsistent event segmentation. Attn. Percept. And Psycho. V 81. D. Lee, H. Ko, J. Kim, and A. Bovik (2021) On the space-time statistics of motion pictures. JOSA A V 38 #7 A. Torralba and A. Oliva (2003) Statistics of natural image categories, Network: Computational Neural Systems 14 391-412 International Electrotechnical Commission, IEC 62087:2008(E), “Methods of measurement for the power consumption of audio, video, and related Equipment. Kunkel T, Daly S. 57-1: Spatiotemporal Noise Targets Inspired by Natural Imagery Statistics. SID Symposium Digest of Technical Papers, 2020, 51:842-845. Kunkel, T, Friedrich, F. Utilizing advanced spatio-temporal backgrounds with dynamic test signals for high dynamic range display metrology. J Soc Inf Display. 2022; 30( 5): 423– 432. https://doi.org/10.1002/jsid.1125 Yiheng Xie1, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, Srinath Sridhar1, "Neural Fields in Visual Computing and Beyond", Eurographics / CGF State-of-the-Art Report, 2022.

Purpose The primary objective of this study examined whether spaced retrieval (SR) using dynamic images (video clips without audio) is more effective than SR using static images to improve face-name recognition in persons with dementia. A secondary objective examined the length of time associations were retained after participants reached criterion. A final objective sought to determine if there is a relationship between SR training and dementia diagnosis. Method A repeated-measures design analyzed whether SR using dynamic images was more effective than SR using static images for face-name recognition. Twelve participants diagnosed with Alzheimer's dementia or vascular dementia were randomly assigned to 2 experimental conditions in which the presentation of images was counterbalanced. Results All participants demonstrated improvement in face-name recognition; there was no significant difference between the dynamic and static images. Eleven of 12 participants retained the information from 1 to 4 weeks post training. Additional analysis revealed a significant interaction effect when diagnoses and images were examined together. Participants with vascular dementia demonstrated improved performance using SR with static images, whereas participants with Alzheimer's dementia displayed improved performance using SR with dynamic images. Conclusions SR using static and/or dynamic images improved face-name recognition in persons with dementia. Further research is warranted to continue exploration of the relationship between dementia diagnosis and SR performance using static and dynamic images.

An ideal quality assessment model should simulate the properties of the visual brain to be consistent with human evaluation. The visual brain appears to have both evolved to seek an efficient, decorrelated representation of image information and to “match” the statistics of the natural image. On one hand, the theoretical studies suggest that sparse representation resembles the strategy in the primary visual cortex of brain for representing natural images. On the other hand, the natural scene statistics have driven the evolution of human visual system and have also inspired the understanding and simulating of visual perception. Inspired by these observations, in this paper, we propose a novel reduced-reference stereoscopic image quality assessment metric using sparse representation and natural scene statistics to simulate the visual perception of the brain. Specifically, the distribution statistics of the classified visual primitives extracted by sparse representation are used to measure the visual information, which is closely related to the hierarchical progressive process of human visual perception. Particularly, the mutual information of classified primitives between two view images is derived as a binocular cue to simulate the binocular fusion process. The maximum mechanism that is applied to select the visual information is a pooling mechanism with which complex cells use the maximal stimuli from a group of simple cells during the transfer process in the primary visual cortex. The natural scene statistics of locally normalized luminance coefficients are used to evaluate the natural losses due to the presence of distortions. The differences of the visual information and the natural scene statistics between the original and distorted images are used to compute the quality score by a prediction function which is trained using support vector regression. Experimental results show that the proposed metric outperforms the state-of-the-art stereoscopic image quality assessment metrics on LIVE 3D IQA database and NBU-MDSID Phase-II database, and delivers competitive performance on Waterloo IVC 3D database.

In this paper, the face image recognition in the application of MATLAB to preprocess the image, application the toolbox classic image processing of the image, through the example to apply MATLAB image processing function, of a given face image processing, and applied to the face recognition system. In this paper, in summing up the face recognition system of analysis of several commonly used image pre processing method based, using MATLAB to realize a set a variety of pre processing method in one of the generic face image pre processing simulation system, the system as the image pre processing module can be embedded in the face recognition system, and the use of image gray histogram Alignment to achieve the recognition of human face images.

Breast cancer is the most common cancer in women. Obtaining the tumor part of breast ultrasound image is of great significance for medical assistance. Breast ultrasound images have the characteristics of variable tumor morphology, more shadows, and blurred borders. Therefore, image preprocessing is usually required before segmentation. However, the traditional image preprocessing method is difficult to effectively distinguish the tumor area and tissue shadow in the ultrasound image, which affects the segmentation result of the tumor part. Therefore, this paper proposes an ultrasound image preprocessing method based on texture features. First, extract the different texture feature images of the breast ultrasound image, and then concatenate the original image and two different texture feature images together to form a new 3-channel RGB image. In the experiment, combining different preprocessing methods and different segmentation methods, the segmentation results of breast ultrasound images are evaluated. Compared with traditional preprocessing methods, the preprocessing methods proposed in this paper have improved in all segmentation evaluation indexes. The experimental results show that the Intersection-over-Union (IoU) and Dice-Similarity-Coefficient (DSC) increased to 0.6022 and 0.7554 respectively.

Our objective is to estimate 3-D human body poses from single 2-D static images. This task is difficult due to the influence of numerous real-world factors such as shading, image noise, occlusions, background clutter and the inherent loss of depth information when a scene is captured onto a 2-D image. We propose a novel fusion of two techniques to form a two-step process: in image preprocessing, an algorithm based on image segmentation and the evaluation of visual cues is used to find immediately identifiable body parts, which we consolidate into 'proposal maps'. This is then fed to a data driven Markov chain Monte Carlo (DDMCMC) pose estimation technique to explore the high dimensional solution space. The best 3-D body pose is then estimated by the maximum a posteriori solution. Experimental results show that the DDMCMC is highly accurate in converging to the true solution when given ideal proposal maps. The results show that the DDMCMC is able to converge to the true solution, albeit with some errors. Nevertheless, the technique shows promise in inferring 3-D body poses. We are currently exploring improvements such as a more accurate model of the human body, the ability to estimate poses from images with cluttered backgrounds and improvement in recognition speed.

In this paper, strategies in image preprocessing, hardware composition and detection methods are considered to improve computerized machine vision detection accuracy. First, image preprocessing and image enhancement are performed to improve the quality of the input image. Second, the hardware composition of the computer vision online inspection system is optimized by focusing on the light source selection and the performance of the image acquisition card in spatial applications. Combined with spatial application calculations, methods such as frequency domain method and Canny operator are used in order to improve the accuracy of machine vision detection. Finally, in the same test environment, the machine vision detection requires only 400MB and the detection accuracy ranges from 85.13% to 99.42%. With these comprehensive strategies, this paper provides a comprehensive and effective approach for computerized machine vision detection in spatial applications to improve detection accuracy and meet demanding application scenarios.

Study question Does embryo quality/viability change over time, suggesting the use of video for AI-based embryo quality assessment has limited benefit over single point-in-time images? Summary answer AI assessment of single static embryo images at multiple time-points indicates embryo viability is dynamic, and past viability is a limited predictor of future pregnancy. What is known already Artificial Intelligence (AI) has been applied to the problem of embryo quality (viability) assessment using either video or single static images. However, whether historical data within video provide an additional advantage over single static images of embryos (at the time of transfer) for assessing embryo viability is not known. This applies to both manual and AI-based embryo assessment. If embryo viability changes over time prior to transfer, then the implication is that the assessment of future pregnancy using historical embryo data from videos would provide limited additional value over single static images taken immediately prior to transfer. Study design, size, duration Retrospective dataset of single embryo images taken at up-to three time-points prior to transfer: Early Day 5, Late Day 5 (8 hours later), and Early Day 6 (16 hours later), with corresponding fetal heartbeat (pregnancy) outcomes. The AI assessed the viability of each embryo at its available timepoints. Viability prediction was compared with pregnancy outcome to assess viability predictiveness at each timepoint prior to transfer, and assess the variability of viability over time. Participants/materials, setting, methods Single static images of 173 embryos were taken using time-lapse incubators from a single IVF clinic. 116 embryos were viable (led to a pregnancy) and 57 were non-viable (did not lead to a pregnancy). The AI was trained on thousands of Day 5 static embryo images taken from multiple IVF laboratories and countries, but was not trained on data from this clinic. Main results and the role of chance When embryos were assessed as viable by the AI immediately prior to transfer (no delay), the AI accuracy (sensitivity) in predicting pregnancy was 88.1% (59/67) for Early Day 5, 84.8% (28/33) for Late Day 5 and 87.5% (14/16) for Early Day 6. When the delay between AI assessment and transfer is 8 hours, 16 hours and 24 hours, the the accuracy drops to 66.7% (22/33), 31.3% (5/16) and 12.5% (2/16), respectively. These results indicate that the viability of the embryo is dynamic, and therefore time series analysis, i.e. using video, may not be well suited for embryo viability assessment because past viability is not necessarily a good predictor of future viability or pregnancy outcome. The viability of the embryo immediately prior to transfer, from a single static image, is a reliable predictor of viability. This is consistent with the current clinical practice of using Gardner score end-point assessment for embryo quality. Results also suggest significant benefits from using time-lapse with AI, where AI continually assesses embryo viability over time using static images. The time point at which the embryo should be transferred to maximize pregnancy outcome is when the embryo has the greatest AI viability score. Limitations, reasons for caution Although evidence suggests past embryo viability is a limited predictor of future pregnancy, a side-by-side comparison of video versus single static image AI assessment would further verify that the historical or change in embryo development or viability has minimal impact on embryo viability assessment at the time prior to transfer. Wider implications of the findings: Time-lapse and AI can beneficially change the way embryos are assessed. Continual AI monitoring of embryos enables optimization of which embryo to transfer and when, to ultimately improve pregnancy outcomes for patients. The findings also suggest that static end-point AI assessment is sufficient for predicting embryo implantation potential. Trial registration number Not applicable

IntroductionIn medical ultrasound education, static and dynamic images help learners to understand sonoanatomical and sonopathological findings. However, there is currently no clear evidence or recommendations from professional associations regarding which presentation format is more effective for developing ultrasound competencies. This prospective, randomised, controlled study aimed to investigate the impact of static versus dynamic ultrasound images on learners’ acquisition of theoretical competencies in abdominal sonography.MethodsParticipants in certified ultrasound courses were randomised into two groups following an introductory session on ultrasound basics. Separately, both groups completed training covering normal findings and pathologies of the gallbladder, the liver and the pancreas. The study group underwent a digital training session (18 min total) for each of the three topics using dynamic images (video clips) while the control group received the same training session using static images. After the training, participants of both groups completed an online multiple-choice theory test, consisting of 54 questions with 4 answer options per question.ResultsA total of 145 datasets (69 control group, 76 study group) were included in the analysis. The study group achieved significantly higher overall theory test scores (p = 0.001) and performed significantly better in the total score of pathology findings (p < 0.001). No significant differences were observed in the total score of normal findings (p = 0.08). Multivariate regression analysis identified “group allocation dynamic,” “experience with > 30 ultrasound examinations,” and “employment in internal medicine” as significant positive predictors (p < 0.01) of theory test performance.ConclusionDynamic images in ultrasound education improve comprehension of pathological findings over static images. These insights should inform the development and adaptation of future training programs and educational materials to enhance the quality of ultrasound education and diagnostic accuracy.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12909-025-07711-9.