Degree Of Blur Research Articles

Particle Tracking of Orientation and Velocity Of Arbitrary Shape

Dispersions or particulate systems are prevalent in numerous natural and practical applications. Characterisation of such multiphase systems usually involves measuring size, spatial location, velocity and number density, which is usually non-trivial. The 'Depth from Defocus' (DFD) technique provides an imaging-based volumetric approach for estimating the size and position of dispersed spherical particles using the blurring information from a shadowgraph image. The two-image DFD approach involves two cameras to capture simultaneous images at different degrees of blur, which, although simpler, requires a mandatory calibration procedure. This study proposes a single-image DFD technique that provides a calibration-free size estimation using theoretical functions while evaluating position requires a simple calibration. The efficacy of this technique is demonstrated for various sparse spherical dispersions, including target dots, dispersed glass beads, liquid droplets in sprays and surface bubble rupture, and pollen grains. The method further enables precise determination of the measurement volume, which is crucial, allowing for bias-free estimates of the size distribution and volume concentration. The simple optical configuration and semi-automatic calibration procedure make this method easily deployable and accessible for diverse applications. The pixelation effect on the limiting value of the degree of blur is also estimated theoretically and compared with experiments. This effect is of interest concerning any typical discrete sensor imaging system in general.

P‐39: Simulation of Perceived Motion Blur on 480Hz OLED Monitor

Refresh rate is an important specification for gaming OLED monitors. With the development of the gaming industry and the graphics processing unit (GPU), the demand for gaming monitors that supporting high refresh rates is increasing. In this paper, simulation methods for perceived motion blur are proposed to predict the degree of blur according to the refresh rate. Experimental results indicate that the proposed simulation methods are quite effective in predicting the degree of motion blur. In addition, this paper presents the predicted results of blur as the refresh rate increases up to 480Hz.

Turbulent image deblurring using a deblurred blur kernel

Abstract In the context of addressing a noisy turbulence-degraded image, it is common to use a denoising low-pass filter before implementing a deblurring algorithm. However, this filter not only suppresses noise but also induces a certain degree of blur into the degraded image. This blur effect causes a blurred estimate of the true blur kernel and ultimately leads to a distorted estimate of the latent clear image. To tackle this issue, this paper presents an innovative single-image deblurring method. It integrates a dedicated blur kernel deblurring step to mitigate the effects of the denoising filter. The L0 norm and L2 norm serve as the respective constraints for latent clear image and blur kernel. Experimental results on both synthetic and real-world turbulence-degraded images demonstrate the effectiveness and efficiency of the proposed method.

A Closer Look at the Reflection Formulation in Single Image Reflection Removal.

How to model the effect of reflection is crucial for single image reflection removal (SIRR) task. Modern SIRR methods usually simplify the reflection formulation with the assumption of linear combination of a transmission layer and a reflection layer. However, the large variations in image content and the real-world picture-taking conditions often result in far more complex reflection. In this paper, we introduce a new screen-blur combination based on two important factors, namely the intensity and the blurriness of reflection, to better characterize the reflection formulation in SIRR. Specifically, we present Screen-blur Reflection Networks (SRNet), which executes the screen-blur formulation in its network design and adapts to the complex reflection on real scenes. Technically, SRNet consists of three components: a blended image generator, a reflection estimator and a reflection removal module. The image generator exploits the screen-blur combination to synthesize the training blended images. The reflection estimator learns the reflection layer and a blur degree that measures the level of blurriness for reflection. The reflection removal module further uses the blended image, blur degree and reflection layer to filter out the transmission layer in a cascaded manner. Superior results on three different SIRR methods are reported when generating the training data on the principle of the screen-blur combination. Moreover, extensive experiments on six datasets quantitatively and qualitatively demonstrate the efficacy of SRNet over the state-of-the-art methods.

Effect of simulated cataract on the accuracy of artificial intelligence in detecting diabetic retinopathy in color fundus photos.

Artificial intelligence (AI) is often trained on images without ocular co-morbidities, limiting its generalizability. This study aims to evaluate the accuracy of a convolutional neural network (CNN) applied to color fundus photos (CFPs) with simulated cataracts (SCs) in detecting diabetic retinopathy (DR). A database of 3662 CFPs (from Asia Pacific Tele-Ophthalmology Society (APTOS) 2019) was used. Using transfer learning, a CNN was trained to classify the training images as either DR or non-DR. The CNN was then applied to classify the testing images after an SC was applied, using varying degrees of Gaussian blur. Accuracy without SC was 97.0%, sensitivity (Sn) 95.7%, specificity (Sp) 98.3%. For mild SC, accuracy was 93.1%, Sn 91.8%, Sp 94.3%. For moderate SC, accuracy was 62.8%, Sn 31.4%, Sp 95.2%. For severe SC, accuracy was 53.5%, Sn 11.8%, Sp 96.5%. SCs significantly impaired AI accuracy. To prepare AI for clinical use, cataracts and other real-world clinical challenges affecting image quality must be accounted for.

Enhancement method for non-uniform scattering images of turbid underwater based on neural network

Due to the influence of light absorption and scattering in the water medium, underwater images often appear with different degrees of blur, color distortion, and so on. The physical model-based method needs to establish image degradation processes and improve image quality by assuming and estimating complex parameters, which limits the generalization ability and practical application effect of these methods. To solve the above problems, this paper proposes an image enhancement method of non-uniform scattering in turbid underwater based on neural network. First, the high-frequency and low-frequency components of underwater images were obtained through the difference of gaussian (DOG) and bilateral filtering (BF), respectively. Then, for the high-frequency component, pulse coupled neural networks (PCNN) to improve the edge details of the image. For low-frequency components, it is carried out in two steps: an adaptive automatic white balance and polynomial regression (AAWBPR) algorithm is proposed, which can effectively eliminate the color bias under different color temperatures and improve the brightness of images; the adaptive color and contrast enhancement (ACCE) method is used to further enhance the color and contrast of images. Finally, Decom-Net and the improved U-Net method are used to reduce the errors caused by the non-uniform scattering effect, weaken the noise interference, and enhance the overall effect of images. Extensive experiments show that the proposed method can effectively improve the quality of turbid underwater non-uniform scattering images, enhance the contrast of images, reduce the complexity of parameters, and has good generalization performance and practical application effects.

Three-dimensional modelling of blur property for conventional optical microscopes

Intensity diffusion caused by optical diffraction limits the imaging resolution of conventional optical microscopes, therefore modelling and measuring the intensity transmission and distribution property of the light sources is a significant research topic in system development and pattern recognition. However, the complicated wave propagation process in optical imaging makes it difficult to provide a direct, analytical and simple mathematical model to measure the relationship between the blur degree and various camera parameters. In this study, an improved intensity transmission and distribution calculation method for conventional optical microscopes was proposed; furthermore, a simple mathematical relation between the blur degree and camera parameters was achieved based on the proposed method. First, the light intensity distribution and propagation characteristics of a conventional optical microscope were modeled based on the property of the Fresnel circular hole diffraction combined with the practical optical parameters. Second, by analyzing the property of intensity distribution and blurring imaging, a quantitative simplified mathematical relationship between the blur degree and camera parameters in optical microscope imaging was obtained, and the three-dimensional (3D) blur property in the optical imaging process was analyzed under different conditions. Third, the connection between diffractive optics and geometric optics was obtained by summarizing and generalizing the 3D blur property curve of each monochromatic light source. Finally, the proposed method was verified through a series of simulations and experiments.

A2OURSR: Adaptive adjustment based real MRI super-resolution via opinion-unaware measurements.

High-quality and high-resolution magnetic resonance (MR) images can provide more details for diagnosis and analyses. Recently, MR images guided neurosurgery has become an emerging technique in clinics. Unlike other medical imaging techniques, it is impossible to achieve both real-time imaging and high image quality in MR imaging. The real-time performance is closely related to the nuclear magnetic equipment itself as well as the collection strategy of the k space data. Optimizing the imaging time cost via the corresponding algorithm is harder than enhancing image quality. Further, in reconstructing low-resolution and noise-rich MR images, getting relatively high-definition and resolution MR images as references are difficult or impossible. In addition, the existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. As a result, severely bad results are inevitable when the modeling assumptions are far apart from the actual situation. To address these problems, we propose a novel adaptive adjustment method based on real MR images via opinion-unaware measurements for real super-resolution (A2OURSR). It can estimate the degree of blur and noise from the test image itself using two scores. These two scores can be considered pseudo labels to train the adaptive adjustable degradation estimation module. Then, the outputs of the above model are used as the inputs of the conditional network to tweak the generated results. Thus, the results can be automatically adjusted via the whole dynamic model. Extensive experimental results show that the proposed A2OURSR is superior to state-of-the-art methods on benchmarks quantitatively and visually.

Evaluation of Dry Eye With Videokeratographer Using a Newly Developed Indicator

To analyze the effectiveness of a newly developed indicator that quantitatively assesses disturbance in Meyer-ring (MR) images obtained via videokeratographer and assess its usefulness for the clinical evaluation of dry eye (DE). Cross-sectional study. This study involved 79 eyes of 79 DE patients (10 males and 69 females; mean age: 62.7 years). After MR images were obtained via videokeratographer, the degree of blur was quantified at multiple points on the ring, with the total value across the cornea being defined as the disturbance value (DV). Correlations between total DV (TDV; the sum of DV for 5 seconds after eye opening) and 12 DE symptoms, Dry Eye-Related Quality of Life Score (DEQS), tear meniscus radius (mm), tear film (TF) lipid-layer spread grade (SG; grades 1-5, 1=best), TF noninvasive breakup time (NIBUT, seconds), fluorescein breakup time (FBUT, seconds), corneal epithelial damage score (CEDS; maximum: 15 points), conjunctival epithelial damage score (CjEDS; maximum: 6 points), and Schirmer 1 test value (mm) were analyzed via univariate and multivariate analyses. No significant correlations were found between TDV and each DE symptom or DEQS, yet significant correlations were found between TDV and SG, NIBUT, FBUT, CEDS, and CjEDS (r=0.56, -0.45, -0.45, 0.72, and 0.62, respectively, all P < .01). TDV was found to be described as 2334+(412.1×CEDS) - (302.0×FBUT) (R2=0.593, P < .0001). Our newly developed indicator, DV, reflecting TF dynamics and stability and corneoconjunctival epithelial damage, may be useful for quantitatively assessing DE ocular-surface abnormalities.

A Semantic Segmentation Method Based on Image Entropy Weighted Spatio-Temporal Fusion for Blade Attachment Recognition of Marine Current Turbines

Marine current turbines (MCTs) may exhibit reduced energy production and structural instability due to attachments, such as biofouling and plankton. Semantic segmentation (SS) is utilized to recognize these attachments, enabling on-demand maintenance towards optimizing power generation efficiency and minimizing maintenance costs. However, the degree of motion blur might vary according to the MCT rotational speed. The SS methods are not robust against such variations, and the recognition accuracy could be significantly reduced. In order to alleviate this problem, the SS method is proposed based on image entropy weighted spatio-temporal fusion (IEWSTF). The method has two features: (1) A spatio-temporal fusion (STF) mechanism is proposed to learn spatio-temporal (ST) features in adjacent frames while conducting feature fusion, thus reducing the impact of motion blur on feature extraction. (2) An image entropy weighting (IEW) mechanism is proposed to adjust the fusion weights adaptively for better fusion effects. The experimental results demonstrate that the proposed method achieves superior recognition performance with MCT datasets with various rotational speeds and is more robust to rotational speed variations than other methods.

All-in-Focus Image Generation from Microscope Focal Stack Images by Deep Learning

In the visual inspection of industrial products using a microscope with a shallow depth of field, it is difficult to capture an all-in-focus image. This paper presents a method to generate an all-in-focus image from a large focal stack, which can be applied to visual inspection. The proposed method adds two improvements to a deep learning method [M. Maximov et al., CVPR, (2020) 1068] that leverages defocus cues in depth estimation. First, it interpolates an index map (a collection of in-focus image indices at all pixels) after the forward pass. It generates an all-in-focus image using all images in focal stacks while reducing the number of images input to the network. Second, the network is trained with synthetic datasets to which a random texture with high-frequency components is added. It helps the network to learn the degree of defocus blur. Experiments using synthetic images and real microscope images show that interpolating the index map and adding texture improves the accuracy of all-in-focus image generation.

Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of blurred aerial remote sensing images. Moreover, the average PSNR of this paper’s algorithm tested on the publicly available dataset RealBlur-R reached 41.02 dB, surpassing the latest SOTA algorithm.

MG-CNFNet: A multiple grained channel normalized fusion networks for medical image deblurring

In different medical imaging techniques, the relative displacement between the patient and detection instrument can lead to different degrees of motion blur, which will affect the diagnosis of the patient’s condition. In this paper, we propose a deblurring model with Multiple Grained Channel Normalized Fusion Networks (MG-CNFNet), which decomposes the image input by multi-grained division to cascade feature extraction from fine to coarse, constructs the encoder using semi-channel self-normalization to enrich the multi-scale information of the image, highlight the main features and improve the deblurring performance, and uses a shallow convolutional fusion block to obtain more shallow information and balance the performance and efficiency of deblurring. Compared with other state-of-the-art methods, our method is qualitatively and quantitatively evaluated on three medical image datasets (including X-ray, Ultrasound, and MRI) to demonstrate its effectiveness in removing motion blur from medical images, and yields better restoration results.

A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method.

This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance.

Successful Detection of the Characteristics of Tear Film Breakup Appearing Immediately after Eye Opening by Videokeratography with a Newly-Developed Indicator.

Spot break (SB), a tear film breakup (TFBU) subtype seen in decreased wettability dry eye (DE), is characterized by a spot-like TFBU that appears immediately after eye opening. It is sometimes difficult to detect using currently available devices for evaluating non-invasive TFBU. The purpose of this study was to investigate the effectiveness of using a newly developed videokeratography indicator for detecting SB. The study involved 44 eyes of 44 DE patients (21 eyes with SB (SB group) and 23 eyes with random break in which fluorescein breakup time was ≤ 5 s (s) (RB group)). All eyes were examined using videokeratography, with digital Meyer-ring images being obtained. By calculation of the degree of luminance blur on the cornea in the Meyer-ring images, termed 'disturbance value' (DV), DVs at 0 s (DV(0)]), 2 s (DV(2)), and 5 s (DV(5)) after eye opening, and the changes of DV between each time, were compared between the SB and RB groups. Results: No significant differences in DV(2) and DV(5) and the rate of change between DV(2) and DV(5) were found between the two groups. However, DV(0) and rate of change between DV(0) and DV(2) in the SB group were significantly greater (p &lt; 0.001) than those in the RB group. SB characteristics were successfully detected by videokeratography using a new videokeratography DV indicator.

Image Object Detection Guided by Blur Degree Evaluation

Image Object Detection Guided by Blur Degree Evaluation

The application of directional derivative in the design of animation characters and background elements

Abstract In the process of designing animation characters and background elements, there may be a high degree of visual blur, resulting in a strong sense of picture distortion, and it is easy to expose the incomplete picture. Accordingly, this paper proposes to use directional derivatives to deal with animation-like visual problems. This method is based on the theoretical basis of directional derivatives, combined with the current mathematical methods of animation character and background element design, and defines the important role of directional derivatives in animation and background design, and more effectively enhances the visual results of animation characters. In the research of clarity and distortion, this paper uses directional derivative derivation as the cutting-in method to test the algorithm for visual simulation and restoration of animated characters and background elements so that the algorithm can be used for each animation character and background element. The defect point is calculated, and the clarity and self-healing ability of the video itself are improved by the influence of the mathematical parameters of the surrounding known points on itself and the key variable of the directional derivative in the field. The results show that the directional derivative can play a role in promoting sublimation in the design of animated characters and background elements.

The role of perceptual difficulty in visual hindsight bias for emotional faces.

Visual hindsight bias, also known as the "saw-it-all-along" effect, is the tendency to overestimate one's perceptual abilities with the aid of outcome knowledge. Recently, Giroux et al. (2022, Emotion, https://doi.org/10.1037/emo0001068 ) reported robust visual hindsight bias for emotional faces except for happy. We examined whether the difficulty of emotional processing could explain their finding. As in Giroux et al., participants saw a blurred image of an emotional face (happy, angry, or neutral) that progressed to clear and were instructed to stop the clearing process when they were able to identify the emotion (foresight trials). They then were shown the clearest image of each face and determined the emotion, followed by a memory task where they were asked to adjust the blur levels to indicate the point at which they had identified the emotion earlier (hindsight trials). Experiment 1 replicated Giroux et al.'s finding, showing that participants stopped the image at a higher degree of blur during the hindsight trials than they had during the foresight trials (i.e., a visual hindsight bias) for the angry and neutral faces but not happy faces. Experiment 2 manipulated the perceptual difficulty of angry and happy faces. While the easy faces replicated the results of Experiment 1, both angry and happy faces produced strong bias when made difficult. A multinomial processing tree model suggests that visual hindsight bias for emotional faces, while robust, is sensitive to perceptual processing difficulties across emotions.

Single Image Deblurring for Pulsed Laser Range-Gated Imaging System with Multi-Slice Integration

The multi-slice integration (MSI) method is one of the approachs to extend the depth of view (DOV) of the pulsed laser range-gated imaging (PLRGI) system. When the DOV is large enough and exceeds the depth of focus of the system, it may make some targets in the image clear and others blurred. In addition, forward scatter is also considered to have a blurring effect on the image. There is very little literature to solve the combined effect of forward scatter and defocus. An imaging model is built based on the model from Jaffe–McGlamery and Fourier optics. According to the imaging model, backscattered light is independent from reflected light from the target, and forward scatter has a relationship with the reflected light. Thus, backscattered light should be removed before deblurring. First, rolling ball and intensity transformation are used to remove the backscattered light and enhance the image. Then, a deep learning model based on Transformer is used to deblur the image. To enable the deep learning model to accommodate different degrees of blurred image, 16 different blur kernels are generated according to the imaging model. Sharp images from a DPDD dataset were chosen to train the model. Images of varying degrees of blur were collected from a water tank and a boat tank by the PLRGI system as test sets. Image deblurring results show that the proposed method can remove different levels of blur and can deal with images which have sharp targets and blurred targets together.

Altered eye movements during reading under degraded viewing conditions: Background luminance, text blur, and text contrast.

Degraded viewing conditions caused by either natural environments or visual disorders lead to slow reading. Here, we systematically investigated how eye movement patterns during reading are affected by degraded viewing conditions in terms of spatial resolution, contrast, and background luminance. Using a high-speed eye tracker, binocular eye movements were obtained from 14 young normally sighted adults. Images of text passages were manipulated with varying degrees of background luminance (1.3–265 cd/m2), text blur (severe blur to no blur), or text contrast (2.6%–100%). We analyzed changes in key eye movement features, such as saccades, microsaccades, regressive saccades, fixations, and return-sweeps across different viewing conditions. No significant changes were observed for the range of tested background luminance values. However, with increasing text blur and decreasing text contrast, we observed a significant decrease in saccade amplitude and velocity, as well as a significant increase in fixation duration, number of fixations, proportion of regressive saccades, microsaccade rate, and duration of return-sweeps. Among all, saccade amplitude, fixation duration, and proportion of regressive saccades turned out to be the most significant contributors to reading speed, together accounting for 90% of variance in reading speed. Our results together showed that, when presented with degraded viewing conditions, the patterns of eye movements during reading were altered accordingly. These findings may suggest that the seemingly deviated eye movements observed in individuals with visual impairments may be in part resulting from active and optimal information acquisition strategies operated when visual sensory input becomes substantially deprived.