Solution of system of the Fredholm integral equation of the first kind

The article is devoted to the study of a system of two inhomogeneous Fredholm integral equations of the first kind with two required functions depending on one variable. Integral equations describe the restoration of a blurred image, production costs, etc. Fredholm integral equations with one desired function have been considered in many works, but relatively few works have been devoted to systems of such equations. The questions of stability for the solution of systems and the construction of a regularizing system of equations were investigated, but the solution was not constructed in an explicit form. In this paper, the kernels depend on two variables. The case is considered: in the kernels and inhomogeneities, the variables are separated in the equations; these functions are decomposed on the basis of two functions on the interval of integration. Examples of basic functions are given. A condition is determined under which the system has a unique solution in the chosen basis, formulated as a theorem. The solution is found in the form of an expansion in this basis. To illustrate the results obtained, an example is considered

Evaluation of blurring and noise of a medical image using a digital phantom

Blurring and noise are an essential characteristic of a medical image on the imaging system. This study shows the characteristics of blurring and noise of a medical image using a digital phantom. A square-shaped digital phantom was produced with pixels that consist of black and white. The line profile was selected on a binary digital image. An image with noise added was generated and a corresponding line profile was also drawn. The degree of noise was increased using the gaussian noise value. The blurring images obtained by applying gaussian blur to a digital phantom was produced similarities to real images. Also, the degree of blurring was increased using the gaussian blur value. As noise increased, the standard deviation of pixels inside and background the object also increased. However, the boundary of the object was retained. As image blurring increased, the boundary of the object was not clearly distinguished. However, the standard deviation of pixels inside and background the object was retained. When extreme noise and blurring are increased, the resulting images are different. For adding noise, the shape is visually maintained. However, the blurred image does not maintain a square shape. Therefore, it is shown that blurring due to movement of object cannot maintain original form. In the image processing method, the reduction of noise is achieved through blur processing. The noise was reduced through blur processing in the image with noise. The degree of noise decreased, but the ambiguity of the boundary increased.

Wer ist Fra Dolcino?

This paper investigates the image of the heretic fra’ Dolcino as presented in both medieval documentary sources and modern literature. Using a wide range of documentary and literary sources, at least two images of fra’ Dolcino can be outlined: the first is the ‚sharply defined‘ image of the legendary figure featuring in literary texts such as the „Divine Comedy“ by Dante Alighieri or „The Name of the Rose“ by Umberto Eco. The second, which emerges from historical documents, is the ‚blurred‘ image of Dolcino from Novara, the leader of a heretical sect that prospered between the 13th and the 14th centuries. This paper offers a study of the events linked to the figure of fra’ Dolcino and to his group of heretics, considering both the historical perspective and the quality of the information conveyed by literary and documentary sources.

Increasing the Image Sharpness of Light Microscope Images Using Deep Learning

Image-based qualitative and quantitative structural analyses using high-resolution light microscopy are integral parts of the materialographic work on materials and components. Vibrations or defocusing often result in blurred image areas, especially in large-scale micrographs and at high magnifications. As the robustness of the image-processing analysis methods is highly dependent on the image grade, the image quality directly affects the quantitative structural analysis. We present a deep learning model which, when using appropriate training data, is capable of increasing the image sharpness of light microscope images. We show that a sharpness correction for blurred images can successfully be performed using deep learning, taking the examples of steels with a bainitic microstructure, non-metallic inclusions in the context of steel purity degree analyses, aluminumsilicon cast alloys, sintered magnets, and lithium-ion batteries. We furthermore examine whether geometric accuracy is ensured in the artificially resharpened images.

Using a Blur Metric to Estimate Linear Motion Blur Parameters

Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.

Smoke Removal and Image Enhancement of Laparoscopic Images by An Artificial Multi-Exposure Image Fusion Method

In laparoscopic surgery, image quality is often degraded by surgical smoke or by side effects of the illumination system, such as reflections, specularities, and non-uniform illumination. The degraded images complicate the work of the surgeons and may lead to errors in image-guided surgery. Existing enhancement algorithms mainly focus on enhancing global image contrast, overlooking local contrast. Here, we propose a new Patch Adaptive Structure Decomposition utilizing the Multi-Exposure Fusion (PASD-MEF) technique to enhance the local contrast of laparoscopic images for better visualization. The set of under-exposure level images are obtained from a single input blurred image by using gamma correction. Spatial linear saturation is applied to enhance image contrast and to adjust the image saturation. The Multi-Exposure Fusion (MEF) is used on a series of multi-exposure images to obtain a single clear and smoke-free fused image. MEF is applied by using adaptive structure decomposition on all image patches. Image entropy based on the texture energy is used to calculate image energy strength. The texture entropy energy determined the patch size that is useful in the decomposition of image structure. The proposed method effectively eliminate smoke and enhance the degraded laparoscopic images. The qualitative results showed that the visual quality of the resultant images is improved and smoke-free. Furthermore, the quantitative scores computed of the metrics: FADE, Blur, JNBM, and Edge Intensity are significantly improved as compared to other existing methods.

An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.

No-Reference Digital Image Quality Assessment Based on Structure Similarity

Blind or non-referential image quality assessment (NR-IQA) indicates the problem of evaluating the visual quality of an image without any reference, Therefore, the need to develop a new measure that does not depend on the reference pristine image. This paper presents a NR-IQA method based on restoration scheme and a structural similarity index measure (SSIM). Specifically, we use blind restoration schemes for blurred images by reblurring the blurred image and then we use it as a reference image. Finally, we use the SSIM as a full reference metric. The experiments performed on standard test images as well as medical images. The results demonstrated that our results using a structural similarity index measure are better than other methods such as spectral kurtosis-based method.