Blurred Images Research Articles

Veracious Interpolated Measure of Angle and Length for Underwater Motion Blurred Images

The quality of the underwater image is impairing with the atmospheric conditions. In this, one of the most significant issues in recent days is due to the motion blur induced by the imaging device or by the movement of the object in underwater image quality degradation. The various parameters of the blurred image must be identified to fix the effect of blurring in post-imaging. Therefore, spectrum-based parameter estimation method is proposed. Initially, to estimate the point spread function (PSF), the angle and the length is measured from image spectrum using radon transform. Then, for the accurate estimation of PSF, Optimized Polynomial Lagrange Interpolation (OPLI) is proposed. The data were collected and analyzed in various natural and structured water bodies in Chennai without affecting the real environment. It is observed that for the underwater images collected, the proposed OPLI approach outperforms compared to few existing traditional estimation methods like cepstral, hough, and radon. Then this veracious interpolated measure of angle and length (VIMAL) is restored using modified Lucy algorithm and is evaluated which results in high performance than the existing classical state-of-the-art methods.

Blurring the boundary between models and reality: Visual perception of scale assessed by performance.

One of the primary jobs of visual perception is to build a three-dimensional representation of the world around us from our flat retinal images. These are a rich source of depth cues but no single one of them can tell us about scale (i.e., absolute depth and size). For example, the pictorial depth cues in a (perfect) scale model are identical to those in the real scene that is being modelled. Here we investigate image blur gradients, which derive naturally from the limited depth of field available for any optical device and can be used to help estimate visual scale. By manipulating image blur artificially to produce what is sometimes called fake tilt shift miniaturization, we provide the first performance-based evidence that human vision uses this cue when making forced-choice judgements about scale (identifying which of an image pair was a photograph of a full-scale railway scene, and which was a 1:76 scale model). The orientation of the blur gradient (relative to the ground plane) proves to be crucial, though its rate of change is less important for our task, suggesting a fairly coarse visual analysis of this image parameter.

Underwater-YCC: Underwater Target Detection Optimization Algorithm Based on YOLOv7

Underwater target detection using optical images is a challenging yet promising area that has witnessed significant progress. However, fuzzy distortions and irregular light absorption in the underwater environment often lead to image blur and color bias, particularly for small targets. Consequently, existing methods have yet to yield satisfactory results. To address this issue, we propose the Underwater-YCC optimization algorithm based on You Only Look Once (YOLO) v7 to enhance the accuracy of detecting small targets underwater. Our algorithm utilizes the Convolutional Block Attention Module (CBAM) to obtain fine-grained semantic information by selecting an optimal position through multiple experiments. Furthermore, we employ the Conv2Former as the Neck component of the network for underwater blurred images. Finally, we apply the Wise-IoU, which is effective in improving detection accuracy by assigning multiple weights between high- and low-quality images. Our experiments on the URPC2020 dataset demonstrate that the Underwater-YCC algorithm achieves a mean Average Precision (mAP) of up to 87.16% in complex underwater environments.

STARRED: a two-channel deconvolution method with Starlet regularization

The spatial resolution of astronomical images is limited by atmospheric turbulence and diffraction in the telescope optics, resulting in blurred images. This makes it difficult to accurately measure the brightness of blended objects because the contributions from adjacent objects are mixed in a time-variable manner due to changes in the atmospheric conditions. However, this effect can be corrected by characterizing the Point Spread Function (PSF), which describes how a point source is blurred on a detector. This function can be estimated from the stars in the field of view, which provides a natural sampling of the PSF across the entire field of view. Once the PSF is estimated, it can be removed from the data through the so-called deconvolution process, leading to images of improved spatial resolution. The deconvolution operation is an ill-posed inverse problem due to noise and pixelization of the data. To solve this problem, regularization is necessary to guarantee the robustness of the solution. Regularization can take the form of a sparse prior, meaning that the recovered solution can be represented with only a few basis eigenvectors. STARRED is a Python package developed in the context of the COSMOGRAIL collaboration and applies to a vast variety of astronomical problems. It proposes to use an isotropic wavelet basis, called Starlets, to regularize the solution of the deconvolution problem. This family of wavelets has been shown to be well-suited to represent astronomical objects. STARRED provides two modules to first reconstruct the PSF, and then perform the deconvolution. It is based on two key concepts: i) the image is reconstructed in two separate channels, one for the point sources and one for the extended sources, and ii) the code relies on the deliberate choice of not completely removing the effect of the PSF, but rather bringing the image to a higher resolution.

Refinement of image quality in panoramic radiography using a generative adversarial network.

We aimed to develop and assess the clinical usefulness of a generative adversarial network (GAN) model for improving image quality in panoramic radiography. Panoramic radiographs obtained at Yonsei University Dental Hospital were randomly selected for study inclusion (n = 100). Datasets with degraded image quality (n = 400) were prepared using four different processing methods: blur, noise, blur with noise, and blur in the anterior teeth region. The images were distributed to the training and test datasets in a ratio of 9:1 for each group. The Pix2Pix GAN model was trained using pairs of the original and degraded image datasets for 100 epochs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were obtained for the test dataset, and two oral and maxillofacial radiologists rated the quality of clinical images. Among the degraded images, the GAN model enabled the greatest improvement in those with blur in the region of the anterior teeth but was least effective in improving images exhibiting blur with noise (PSNR, 36.27 > 32.74; SSIM, 0.90 > 0.82). While the mean clinical image quality score of the original radiographs was 44.6 out of 46.0, the highest and lowest predicted scores were observed in the blur (45.2) and noise (36.0) groups. The GAN model developed in this study has the potential to improve panoramic radiographs with degraded image quality, both quantitatively and qualitatively. As the model performs better in refining blurred images, further research is required to identify the most effective methods for handling noisy images.

Program Dekonvolusi Blind Berbasis Matlab untuk Mempertajam Citra Akibat Simulasi Efek Buram Lensa

Blurred image due to lens defocus can be simulated mathematically as a convolution result between sharp image and Point Spread Function of blur (unfocus) lens. Image sharpening is the opposite of image blurring which can be done by deconvolution. This research was conducted to simulate digital image sharpening using the Blind deconvolution method. The Point Spread Function of the lens effect is used as the deconvolution Kernel function. This Point Spread Function is modeled mathematically with the Gaussian function approach. Convolution results between digital images from photos of an object are convoluted with the point spread function to produce a blurry image. The blurry image is then sharpened using the Blind convolution method. The slight difference between the deconvolution result image and the original object photo image indicates that the program is running well. Peak Signal to Noise Ratio (PSNR) is used to determine image sharpening recovery. Optimal sharpening recovery from deconvolution iterations is obtained at the maximum PSNR value

Моделирование процесса распознавания символов в нормативных документах организации

Improving the quality of classification of different documents is a purpose of modeling the optical character recognition. Non-digital documents, such as scanned or photographed documents, are difficult to classify correctly in electronic document management systems. A decision was made to simulate the process of optical character recognition in the regulatory documents of the organization. There have been considered various methods of modeling the process. The structure of departments for the electronic document management system is given. Methods of implementing optical character recognition (OCR) are considered. The stages of the OCR system development are revealed: image processing, segmentation, recognition. The methods of image processing are analyzed. The main processes associated with image processing are disclosed: alignment, blurring, binarization, finding contours, removing extra lines. Comparison of image blur methods is made. Two stages of image binarization are defined: conversion of a color image into a gray image, binarization of a gray image. The Kenny operator is proposed as a second stage of binarization, which is used to detect the boundaries of the image. The last stage of image processing is the process of removing extra lines. Algorithms for dividing text areas into segments are considered. 3 stages of segmentation are identified: string segmentation, word segmentation, character segmentation. A segmentation algorithm is defined based on calculating the average brightness of image pixels to search for different intervals: line spacing, word spacing, character spacing. Available popular online OCR services as well as some popular desktop programs are considered. A connection has been found between an artificial neural network and optical object recognition. To implement the recognition stage, it is proposed to use an artificial neural network.

Identification of blurred terahertz images by improved cross-layer convolutional neural network.

Terahertz imaging technology has been gradually used in space communication, radar detection, aerospace and biomedical fields. Nevertheless, there are still some limits in terahertz image, such as single tone, fuzzy texture features, poor image resolution and less data, which seriously affect the application and popularization of Terahertz image technology in many fields. Traditional convolutional neural network (CNN) is an effective method for image recognition, but it is limited in highly blurred terahertz image recognition due to the great difference between terahertz image and traditional optical image. This paper presents a proven method for higher recognition rate of blurred terahertz images by using an improved Cross-Layer CNN model with different definition terahertz image dataset. Compared to employing clear image dataset, the accuracy of blurred image recognition can be improved from about 32% to 90% with different definition dataset. Meanwhile, the recognition accuracy of high blurred image can be improved by approximately 5% in contrast to the traditional CNN, which makes the higher recognition ability of neural network. It can be demonstrated that various types of blurred terahertz imaging data can be effectively identified by constructing different definition dataset combined with Cross-Layer CNN. A new method is proved to improve the recognition accuracy of terahertz imaging and application robustness in real scenarios.

A Novel Single X-Wire AM Mini-LED Backlight Unit with Motion Blur Reduction for High-Image-Quality LCD TVs

We have demonstrated a high-image-quality 85-inch 4 K high-dynamic-range (HDR) LCD display with 2304 dimming zones using a single X-wire AM mini-LED backlight unit (BLU) with motion blur and residual image reduction technology. Our latest work proposed the new single X-wire AM driving method, consisting of one matrix control IC/dimming controller (DCON) and X-wires of mini-LED driver ICs (dimmers), which eliminates extra scanning, sensing lines and pixel/source driver IC using an adaptive addressing dimming method. Additionally, a single X-wire controlled system can realize a flexible and expandable layout, which can achieve ultra thin and low cost in ultra-high dimming zones and large size display. Furthermore, the horizontal-scan (H-scan) driving method, which reduces motion blur and residual images using a scanning black frame insertion (BFI) technology, is utilized. The proposed mini-LED BLU can save about 9% power consumption compared to the conventional one in normal displays.

SDAN-MD: Supervised dual attention network for multi-stage motion deblurring in frontal-viewing vehicle-camera images

Motion blur in images usually distorts the information of objects, thus degrading the performance of semantic segmentation. However, there is no previous research on improving the segmentation performance by restoring the frontal-viewing vehicle-camera images taken under motion blur. Therefore, this study proposes a supervised dual attention network for multi-stage motion deblurring (SDAN-MD) for this task. In SDAN-MD, a supervised dual attention module (SDAM) is proposed, which adopts the supervised spatial and channel attention mechanisms to provide a supervisory signal of ground truth. In addition to Charbonnier loss and edge loss, we use perceptual loss utilizing Euclidean distance based on feature maps obtained from the segmentation network.Experiments were conducted with the motion blurred databases from the two open databases of road scene, Cambridge driving Labeled Video Database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI).The results show that the proposed SDAN-MD achieves 92.89% and 87.27% pixel accuracies in semantic segmentation using these two databases, respectively, outperforming the state-of-the-art methods.

A progressive framework for rotary motion deblurring

The rotary motion deblurring is an inevitable procedure when the imaging seeker is mounted in the rotating missiles. Traditional rotary motion deblurring methods suffer from ringing artifacts and noise, especially for large blur extents. To solve the above problems, we propose a progressive rotary motion deblurring framework consisting of a coarse deblurring stage and a refinement stage. In the first stage, we design an adaptive blur extents factor (BE factor) to balance noise suppression and details reconstruction. And a novel deconvolution model is proposed based on BE factor. In the second stage, a triple-scale deformable module CNN(TDM-CNN) is designed to reduce the ringing artifacts, which can exploit the 2D information of an image and adaptively adjust spatial sampling locations. To establish a standard evaluation benchmark, a real-world rotary motion blur dataset is proposed and released, which includes rotary blurred images and corresponding ground truth images with different blur angles. Experimental results demonstrate that the proposed method outperforms the state-of-the-art models on synthetic and real-world rotary motion blur datasets. The code and dataset are available at https://github.com/Jinhui-Qin/RotaryDeblurring.

Fluorescence image deconvolution microscopy via generative adversarial learning (FluoGAN)

We propose FluoGAN, an unsupervised hybrid approach combining the physical modelling of fluorescence microscopy timelapse acquisitions with a generative adversarial learning procedure for the problem of image deconvolution. Differently from standard approaches combining a least-square data term based on one (long-time exposure) image with sparsity-promoting regularisation terms, FluoGAN relies on a data term being the distributional distance between the fluctuating observed timelapse (short-time exposure images) and the generative model. Such distance is computed by adversarial training of two competing architectures: a physics-inspired generator simulating the fluctuating behaviour as a Poisson process of the observed images combined with blur and undersampling, and a standard convolutional discriminator network. FluoGAN is a fully unsupervised approach requiring only a fluctuating sequence of blurred, undersampled and noisy images of the sample of interest as input. It can be complemented with prior knowledge on the desired solution such as sparsity, non-negativity etc. After having described the main ideas behind FluoGAN, we formulate the corresponding optimisation problem and report several results on simulated and real phantoms used by microscopy engineers to quantitatively assess spatial resolution. The comparison of FluoGAN with state-of-the-art methodologies shows improved resolution, allowing for high-precision reconstructions of fine structures in challenging real Ostreopsis cf Ovata data. The FluoGAN code is available at: https://github.com/cmayeul/FluoGAN.

Autostereoscopic 3D Augmented Reality Navigation for Laparoscopic Surgery: A Preliminary Assessment.

Since Augmented Reality (AR) and 3D visualization have proven to be of great significance to the safety and effectiveness of surgical outcomes, will autostereoscopic 3D AR without glasses bring new opportunities for surgical navigation of laparoscopic surgery? We used the CPD-based deformation registration algorithm and the proposed virtual view generation algorithm, realizing a deformable autostereoscopic 3D AR navigation framework for laparoscopic surgery. The depth perception and user experience of the 3D AR navigation were evaluated compared with the 2D AR display using an in-vitro porcine heart and offline clinical partial nephrectomy laparoscopic images. The autostereoscopic 3D AR allowed participants to have a more consistent spatial perception as well as a shorter measuring time than 2D AR with significant difference of p < 0.05. It can also improve relative depth perception for smaller distance separation of objects < = 3.28 mm. However, the autostereoscopic 3D AR perceived a worse experience compared to 2D AR in the user experience. Autostereoscopic 3D AR shows a more efficient and robust sense of spatial scale than 2D AR with better potential to shorten the operating time and improve surgical outcomes than 2D AR, but image blur and distortion are issues that must be solved to improve the perception effect. High precise registration and high fluency visualization requirements could make autostereoscopic 3D AR navigation for soft tissue more challenging. Our work lays a theoretical foundation for the further development of laparoscopic surgical navigation.

モンテカルロフィルタに基づく眼球回旋運動の計測法

In recent years, various studies on anthropometry using video images have been actively conducted. Of those applications, the measurement of eye movements is expected in fields such as medical care, and human interface. The eye movements include horizontal and vertical translations due to the movement of the gaze and torsional eye movements. This paper proposes a method for accurately measuring the torsional eye movement, which is generally considered difficult to measure with high accuracy. We have improved the Monte Carlo filter, which was traditionally used for object tracking in translational motion, so that it can also measure rotational motion. Evaluation experiments indicated that the measurement error using the proposed method was in a range between 0.07±0.49° to -0.03±0.54°, while the measurement accuracy of the conventional matching method was 0.28±0.81°. In addition, the results suggest that the proposed method can measure even a blurred iris image with a relatively small error.

Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-the-art approaches.

Non-defocus high transmittance image slicer.

The first non-defocus high transmittance non-fiber image slicer is presented. In order to solve the problem of image blur caused by the defocus between different sliced sub-images, an optical path compensation method based on stepped prism plate is proposed. Design results show that both the maximal defocus amount between the four sliced sub-images is reduced from 2.363 mm to nearly 0. The diameter of the dispersion spot on the focal plane is reduced from 98.47 μm to close to 0. The optical transmittance of the image slicer is up to 91.89%. This new image slicer is greatly valuable for high resolution and high transmittance spectrometer.

Sub-resolution modeling of the apparent mass loss in quantitative broadband X-ray radiography

The quantitative impact of image blur on calculated object mass in X-ray radiography is explored. Radiographed object masses are initially estimated through experimental calibration and compared against their known true mass, with the deviation, or “apparent mass loss”. Apparent mass loss occurs due to the effect of spatial blur on X-ray transmission images that reduces the measured mass because while the image intensity is conserved with spatial blur, the nonlinear conversion to path length is not. A synthetic model is proposed where an object’s apparent mass loss is estimated through the amount of blur present within the radiograph image. The image blur is inferred through a combination of readily available image parameters related to the signal levels, object shape, and experimental extrinsics. A regularized regression model is built that correlates these blur parameters to the expected mass loss. The model is first trained on 2100 and tested on 140 synthetically created objects, and then later verified on two separate experimental setups of type 304 stainless steel objects imaged with a portable tube source at 150 kVp and type 6061 aluminum objects at 80 kVp. The proposed model allows for a simple post hoc reduction in errors of approximately 20% in object mass through X-ray radiography.

Analysis of Image Processing Strategies Dedicated to Underwater Scenarios

Underwater images undergo quality degradation issues of an image, like blur image, poor contrast, non-uniform illumination etc. Therefore, to process these degraded images, image processing come into existence. In this paper, two important image processing methods namely Image restoration and Image enhancement are compared. This paper also discusses the quality measures parameters of image processing which will be helpful to see clear images.

Reconstruction of Blur Images for Quality Enhancement

Reconstruction of Blur Images for Quality Enhancement

Underwater image enhancement based on zero-shot learning and level adjustment

Light is scattered and partially absorbed while traveling through water, hence, underwater captured images often exhibit issues such as low contrast, detail blurring, color attenuation, and low illumination. To improve the visual performance of underwater imaging, herein, we propose a two-step method of zero-shot dehazing and level adjustment. In the newly developed approach, the original image is fed into a “zero-shot” dehazing network and further enhanced by an improved level adjustment methodology combined with auto-contrast. By conducting experiments, we then compare the performance of the proposed method with six classical state-of-the-art methods. The qualitative results confirm that the proposed method is capable of effectively removing haze, correcting color deviations, and maintaining the naturalness of images. We further perform a quantitative evaluation, revealing that the proposed method outperforms the comparison methods in terms of peak signal-to-noise ratio and structural similarity. The enhancement results are also measured by employing the underwater color image quality evaluation index (UCIQE), indicating that the proposed approach exhibits the highest mean values of 0.58 and 0.53 on the two data sets. The experimental results collectively validate the efficiency of the proposed methodology in enhancing underwater blurred images.