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Artykuły w czasopismach na temat „Lowlight Restored Image Quality”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 31 lipca 2025

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1

Refhiansyah, Anggie Irfhan, Didi Suhaedi, and Yurika Permanasari. "Penggunaan Topsis untuk Menentukan Exposure Terbaik pada Kamera yang Memiliki Sensor M4/3." Bandung Conference Series: Mathematics 1, no. 1 (2021): 1–6. http://dx.doi.org/10.29313/bcsm.v1i1.12.

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Abstract. Cameras that have small sensors such as Micro Four Thirds (M4/3) are experiencing increasing interest in the market today, this is due to the small and compact size of the camera. However, the weakness of cameras with small sensors is that the quality is not as good as those of cameras with larger sensors, especially in lowlight situations because the electronic noise (noise) increases at high ISOs, making it quite difficult to determine the best exposure in these situations. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to select the best

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Malyugina, Alexandra, Nantheera Anantrasirichai, and David Bull. "Wavelet-Based Topological Loss for Low-Light Image Denoising." Sensors 25, no. 7 (2025): 2047. https://doi.org/10.3390/s25072047.

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Despite significant advances in image denoising, most algorithms rely on supervised learning, with their performance largely dependent on the quality and diversity of training data. It is widely assumed that digital image distortions are caused by spatially invariant Additive White Gaussian Noise (AWGN). However, the analysis of real-world data suggests that this assumption is invalid. Therefore, this paper tackles image corruption by real noise, providing a framework to capture and utilise the underlying structural information of an image along with the spatial information conventionally used

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Nhu. "PARAMETRIC BLIND-DECONVOLUTION METHOD TO REMOVE IMAGE ARTIFACTS IN WAVEFRONT CODING IMAGING SYSTEMS." Journal of Military Science and Technology, no. 72A (May 10, 2021): 62–68. http://dx.doi.org/10.54939/1859-1043.j.mst.72a.2021.62-68.

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Wavefront coding technique includes a phase mask of asymmetric phase mask kind in the pupil plane to extend the depth of field of an imaging system and the digital processing step to obtain the restored final high-quality image. However, the main drawback of wavefront coding technique is image artifacts on the restored final images. In this paper, we proposed a parameter blind-deconvolution method based on maximizing of the variance of the histogram of restored final images that enables to obtain the restored final image with artifact-free over a large range of defocus.

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Sun, Guoxin, Xiong Yan, Huizhe Wang, et al. "Color restoration based on digital pathology image." PLOS ONE 18, no. 6 (2023): e0287704. http://dx.doi.org/10.1371/journal.pone.0287704.

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Objective Protective color restoration of faded digital pathology images based on color transfer algorithm. Methods Twenty fresh tissue samples of invasive breast cancer from the pathology department of Qingdao Central Hospital in 2021 were screened. After HE staining, HE stained sections were irradiated with sunlight to simulate natural fading, and every 7 days was a fading cycle, and a total of 8 cycles were experienced. At the end of each cycle, the sections were digitally scanned to retain clear images, and the color changes of the sections during the fading process were recorded. The colo

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Zhang, Hua, Yi Kai Shi, Kui Dong Huang, and Qing Chao Yu. "Image Restoration Method Based on Pre-Filtering for Cone-Beam Computed Tomography." Applied Mechanics and Materials 229-231 (November 2012): 1858–61. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1858.

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For the problem of image quality degradation of cone-beam Computed Tomography (CBCT) based on flat panel detector (FPD), a constrained least squares iteration (CLSI) restoration method based on pre-filtering is proposed. Firstly, the original projected images are denoised with bilateral filtering algorithm. Then, the denoised projected images are restored with CLSI. Finally, the final restored images are obtained by adding the noise images, which got by subtracting the projected images before and after denoising, to the restored images. The experimental results show that the method well inhibi

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Xie, Wang, and Li. "A Fragile Watermark Scheme for Image Recovery Based on Singular Value Decomposition, Edge Detection and Median Filter." Applied Sciences 9, no. 15 (2019): 3020. http://dx.doi.org/10.3390/app9153020.

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Many fragile watermark methods have been proposed for image recovery and their performance has been greatly improved. However, jagged edges and confusion still exist in the restored areas and these problems need to be solved to achieve a better visual effect. In this paper, a method for improving recovery quality is proposed that adopts singular value decomposition (SVD) and edge detection for tamper detection and then uses a median filter for image recovery. Variable watermark information can be generated that corresponds to block classifications. With mapping and neighborhood adjustment, the

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Ozsahin, Dilber Uzun, Abubakar Adamu, Maryam Rabiu Aliyu, and Huzaifa Umar. "A modified Tseng algorithm approach to restoring thoracic diseases’ computerized tomography images." PLOS ONE 19, no. 7 (2024): e0305728. http://dx.doi.org/10.1371/journal.pone.0305728.

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It is well-known that the Tseng algorithm and its modifications have been successfully employed in approximating zeros of the sum of monotone operators. In this study, we restored various thoracic diseases’ computerized tomography (CT) images, which were degraded with a known blur function and additive noise, using a modified Tseng algorithm. The test images used in the study depict calcification of the Aorta, Subcutaneous Emphysema, Tortuous Aorta, Pneumomediastinum, and Pneumoperitoneum. Additionally, we employed well-known image restoration tools to enhance image quality and compared the qu

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Irshad, Muhammad, Camilo Sanchez-Ferreira, Sana Alamgeer, Carlos H. Llanos, and Mylène C. Q. Farias. "No-reference Image Quality Assessment of Underwater Images Using Multi-Scale Salient Local Binary Patterns." Electronic Imaging 2021, no. 9 (2021): 265–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-265.

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Images acquired in underwater scenarios may contain severe distortions due to light absorption and scattering, color distortion, poor visibility, and contrast reduction. Because of these degradations, researchers have proposed several algorithms to restore or enhance underwater images. One way to assess these algorithms’ performance is to measure the quality of the restored/enhanced underwater images. Unfortunately, since reference (pristine) images are often not available, designing no-reference (blind) image quality metrics for this type of scenario is still a challenge. In fact, although th

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Lee, Jonghyeok, Hyunwoo Lim, Sanghyun Cho, Changwoo Seo, Bo Kyung Cha, and Hyosung Cho. "Analytic sparse-view CBCT reconstruction using a novel sinogram restoration method based on higher-order Fourier harmonic peaks." Journal of Instrumentation 19, no. 11 (2024): C11001. http://dx.doi.org/10.1088/1748-0221/19/11/c11001.

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Abstract This study presents an analytic sparse-view cone-beam computed tomography (CBCT) reconstruction method using a novel sinogram restoration technique based on higher-order Fourier harmonic peaks to obtain CBCT images of reasonable quality with a reduced radiation dose. The proposed method involves four main steps: (1) acquiring sparse-view (P144) sinogram from a CBCT system, (2) upsampling (P720) the original sparse-view sinogram with equally spaced zero padding, (3) separating higher-order Fourier harmonic peaks of the upsampled sinogram using a band-pass filter, and (4) restoring the

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Xu, Zijie, and Erfu Wang. "An Algorithm for Detecting and Restoring Tampered Images Using Chaotic Watermark Embedding." Electronics 13, no. 18 (2024): 3604. http://dx.doi.org/10.3390/electronics13183604.

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In recent years, the advancement of digital image processing technology and the proliferation of image editing software have reduced the technical barriers to digital image processing, enabling individuals without professional training to modify and edit images at their discretion. Consequently, the integrity and authenticity of the original image content assume greater significance. The current techniques for detecting tampering in watermark embedding are inadequate in terms of security, efficiency, and image restoration quality. In light of the aforementioned considerations, this paper puts

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Rezgui, Hicham, Messaoud Maouni, Mohammed Lakhdar Hadji, and Ghassen Touil. "Three robust edges stopping functions for image denoising." Boletim da Sociedade Paranaense de Matemática 40 (January 20, 2022): 1–12. http://dx.doi.org/10.5269/bspm.45945.

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In this paper, we present three strong edge stopping functions for image enhancement. These edge stopping functions have the advantage of effectively removing the image noise while preserving the true edges and other important features. The obtained results show an improved quality for the restored images compared to existing restoration models.

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Wang, Huai Sheng. "A No Interference Optical Image Encryption by a Fresnel Diffraction and a Fourier Transformation." Advanced Materials Research 459 (January 2012): 461–64. http://dx.doi.org/10.4028/www.scientific.net/amr.459.461.

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A no interference optical image encryption is put forward in this paper. The encrypting process is composed of a Fresnel diffraction and a Fourier transformation. A digital image coded with a random phase plate first takes a Fresnel diffraction. The diffraction function is enlarged and coded with another random phase mask. At last the enlarged function undergoes a Fourier transformation. The real part of the transformed function is defined as an encrypted image. In decrypting process, first the encrypted image takes an inverse Fourier transformation. Then the upper left corner of the transform

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Esteban, Allen Paul, Prithviraj Singh Chouhan, Aman Ahlawat, and Tarunika Dursinhbhai Chaudhari. "MULTIFRAME IMAGE RESTORATION - ENHANCING IMAGE QUALITY THROUGH ADVANCED RECONSTRUCTION TECHNIQUES." ICTACT Journal on Image and Video Processing 15, no. 2 (2024): 3433–40. https://doi.org/10.21917/ijivp.2024.0486.

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The degradation of image quality due to noise, blur, and low contrast remains a significant challenge in various imaging applications, particularly in medical diagnostics, remote sensing, and surveillance. Effective restoration of such images is essential to enhance visual clarity and extract meaningful information. Conventional techniques often struggle to balance noise reduction and detail preservation. To address these limitations, this study proposes an advanced multiframe image restoration approach combining Contrast Limited Adaptive Histogram Equalization (CLAHE) and Deep Belief Networks

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Zhu, Xifang, Ruxi Xiang, Feng Wu, and Xiaoyan Jiang. "Single image haze removal based on fusion darkness channel prior." Modern Physics Letters B 31, no. 19-21 (2017): 1740037. http://dx.doi.org/10.1142/s0217984917400371.

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To improve the image quality and compensate deficiencies of haze removal, we presented a novel fusion method. By analyzing the darkness channel of each method, the effective darkness channel model that takes the correlation information of each darkness channel into account was constructed. This method was used to estimate the transmission map of the input image, and refined by the modified guided filter in order to further improve the image quality. Finally, the radiance image was restored by combining the monochrome atmospheric scattering model. Experimental results show that the proposed met

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Karam, Ghada Sabah. "Blurred Image Restoration with Unknown Point Spread Function." Al-Mustansiriyah Journal of Science 29, no. 1 (2018): 189. http://dx.doi.org/10.23851/mjs.v29i1.335.

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Blurring image caused by a number of factors such as de focus, motion, and limited sensor resolution. Most of existing blind deconvolution research concentrates at recovering a single blurring kernel for the entire image. We proposed adaptive blind- non reference image quality assessment method for estimation the blur function (i.e. point spread function PSF) from the image acquired under low-lighting conditions and defocus images using Bayesian Blind Deconvolution. It is based on predicting a sharp version of a blurry inter image and uses the two images to solve a PSF. The estimation down by

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Mansoori, M. A., M. R. Mosavi, and M. H. Bisjerdi. "Regularization-Based Semi-Blind Image Deconvolution Using an Improved Function for PMMW Images Application." Journal of Circuits, Systems and Computers 27, no. 07 (2018): 1850107. http://dx.doi.org/10.1142/s0218126618501074.

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Image deconvolution is a method for reversing the distortion in an imaging system. It is widely used in removing blur and noise from a degraded image. This is an ill-posed inverse problem, and one should use regularization techniques to solve this problem. Regularization functions play an important role in finding the desired solution. In this paper, a new function in wavelet domain is proposed, which is useful in blind deconvolution. In addition, a simple algorithm is used to obtain the restored image and unknown point spread function. The proposed approach is tested on three standard images

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Котов, Денис. "КРИТЕРІАЛЬНІ ПОКАЗНИКИ ЯКОСТІ ОБРОБКИ МУЛЬТИСЕНСОРНОЇ ІНФОРМАЦІЇ В УМОВАХ ДЕСТАБІЛІЗУЮЧИХ ВПЛИВІВ". Collection of scientific works of Odesa Military Academy, № 18 (3 березня 2023): 119–26. http://dx.doi.org/10.37129/2313-7509.2022.18.119-126.

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The approach to determining the criterion indicators of the quality of digital information processing in the multi-sensor information-controlled system of the car under the influence of destabilizing factors is considered. It was established that the process of processing information that enters the information-controlled system in digital form from various sensors involves the calculation of discrete convolutions of matrices of relevant operators and arrays of initial data. This process includes procedures for forming an array of responses to the observed information flow, an array of respons

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Wang, Kun Ling. "The Image Restoration Method Based on Patch Sparsity Propagation in Big Data Environment." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 7 (2018): 1072–76. http://dx.doi.org/10.20965/jaciii.2018.p1072.

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The traditional image restoration method only uses the original image data as a dictionary to make sparse representation of the pending blocks, which leads to the poor adaptation of the dictionary and the blurred image of the restoration. And only the effective information around the restored block is used for sparse coding, without considering the characteristics of image blocks, and the prior knowledge is limited. Therefore, in the big data environment, a new method of image restoration based on structural coefficient propagation is proposed. The clustering method is used to divide the image

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M, Velammal, Thiyam Ibungomacha Singh, Nilesh Madhukar Patil, and Subharun Pal. "MULTIFRAME IMAGE RESTORATION USING GENERATIVE ADVERSARIAL NETWORKS." ICTACT Journal on Image and Video Processing 14, no. 1 (2023): 3043–48. https://doi.org/10.21917/ijivp.2023.0432.

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This paper introduces a novel approach for multiframe image restoration using Generative Adversarial Networks (GANs). Traditional image restoration techniques often struggle with handling complex degradation patterns and noise in images. In contrast, GANs have demonstrated remarkable capability in generating realistic and high-quality images. The proposed method leverages the power of GANs to restore multiframe degraded images by training the generator to learn the underlying clean image from a set of degraded frames. The discriminator collaborates with the generator to ensure the fidelity of

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Shemiakina, Julia, Elena Limonova, Natalya Skoryukina, Vladimir V. Arlazarov, and Dmitry P. Nikolaev. "A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents." Mathematics 9, no. 17 (2021): 2155. http://dx.doi.org/10.3390/math9172155.

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In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of

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Wieslander, Håkan, Carolina Wählby, and Ida-Maria Sintorn. "TEM image restoration from fast image streams." PLOS ONE 16, no. 2 (2021): e0246336. http://dx.doi.org/10.1371/journal.pone.0246336.

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Microscopy imaging experiments generate vast amounts of data, and there is a high demand for smart acquisition and analysis methods. This is especially true for transmission electron microscopy (TEM) where terabytes of data are produced if imaging a full sample at high resolution, and analysis can take several hours. One way to tackle this issue is to collect a continuous stream of low resolution images whilst moving the sample under the microscope, and thereafter use this data to find the parts of the sample deemed most valuable for high-resolution imaging. However, such image streams are deg

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Wang, Zhiyu, Jiayan Zhuang, Sichao Ye, Ningyuan Xu, Jiangjian Xiao, and Chengbin Peng. "Image Restoration Quality Assessment Based on Regional Differential Information Entropy." Entropy 25, no. 1 (2023): 144. http://dx.doi.org/10.3390/e25010144.

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With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality a

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Raihan A, Jarina, Pg Abas, and Liyanage De Silva. "Role of Restored Underwater Images in Underwater Imaging Applications." Applied System Innovation 4, no. 4 (2021): 96. http://dx.doi.org/10.3390/asi4040096.

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Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method ha

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Jiao, Qingliang, Ming Liu, Pengyu Li, et al. "Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization." Journal of Marine Science and Engineering 9, no. 6 (2021): 570. http://dx.doi.org/10.3390/jmse9060570.

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The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of

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Bavrina, A. Y., and V. A. Fedoseev. "Semi-fragile watermarking with recovery capabilities for HGI compression method." Computer Optics 46, no. 1 (2022): 103–12. http://dx.doi.org/10.18287/2412-6179-co-1021.

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The article proposes a new semi-fragile watermarking system with the ability of tamper localization and recovery after distortions, adapted for the HGI image compression method. The system uses a hierarchical image structure when embedding and replaces the stage of post-interpolation residuals quantization with a special quantizer based on quantization index modulation. As a result, the protected image becomes resistant to HGI compression with an adjustable quality parameter. The proposed watermarking system allows an image to be restored after distortions with an acceptable quality. In this c

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Sai, S. V. "A method for assessing photorealistic image quality with high resolution." Computer Optics 46, no. 1 (2022): 121–29. http://dx.doi.org/10.18287/2412-6179-co-899.

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The article proposes a method for assessing photorealistic image quality based on a comparison of the detail coefficients in the original and distorted images. An algorithm for identifying fine structures of the original image uses operations of active pixels segmentation, which include point objects, thin lines and texture fragments. The number of active pixels is estimated by the value of a fine detail factor (FDF), which is determined by the ratio of active pixels to the total number of image pixels. The same algorithm is used to calculate the FDF of the distorted image and, further, the im

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Wang, Shuai, Huiqin Rong, Chunyuan He, Libo Zhong, and Changhui Rao. "Multiframe Correction Blind Deconvolution for Solar Image Restoration." Publications of the Astronomical Society of the Pacific 134, no. 1036 (2022): 064502. http://dx.doi.org/10.1088/1538-3873/ac6445.

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Abstract A series of short-exposure images are often used for rich, small-scale structure, high-quality, and high-resolution astronomical observations. Postprocessing of the closed-loop adaptive optics (AO) image using ground-based astronomical telescopes plays an important role in astronomical observations due to it further improving image quality after AO processing. These images show several main characteristics: random spatial variation blur kernel, unclear model after AO correction, unclear physical characteristics of observation objects, etc. Our goal is to propose a multiframe correctio

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Liao, Qingtao. "Research on Medical Image Denoising Algorithm Based on Deep Learning Image Quality Evaluation." Journal of Medical Imaging and Health Informatics 11, no. 5 (2021): 1384–93. http://dx.doi.org/10.1166/jmihi.2021.3387.

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Improving the clarity of medical images is of great significance for doctors to quickly diagnose and analyze the disease. However, the existing image denoising algorithms largely depend on the size of the data set, the optimization effect of the loss function, and the difficulty in adjusting the parameters. Therefore, a medical image denoising algorithm based on deep learning image quality evaluation is proposed. First, the convolution layer of the convolutional neural network and the output of the first full connection layer are used as the perception features. By stacking the perception loss

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Ávila, Francisco J., Jorge Ares, María C. Marcellán, María V. Collados, and Laura Remón. "Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images." Journal of Imaging 7, no. 4 (2021): 73. http://dx.doi.org/10.3390/jimaging7040073.

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The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium in which the light travels from the object to the final imaging sensor. The analysis of the point spread function of the optical system is an objective way to quantify the image degradation. In retinal imaging, the presence of corneal or cristalline lens opacifications spread the light at wide angular distributions. If the mathematical operator that degrades the image is known, the image can be restored through deconvolution methods. In the particular case of r

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Gao, Yakun, Haibin Li, and Shuhuan Wen. "Restoration and Enhancement of Underwater Images Based on Bright Channel Prior." Mathematical Problems in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/3141478.

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This paper proposed a new method of underwater images restoration and enhancement which was inspired by the dark channel prior in image dehazing field. Firstly, we proposed the bright channel prior of underwater environment. By estimating and rectifying the bright channel image, estimating the atmospheric light, and estimating and refining the transmittance image, eventually underwater images were restored. Secondly, in order to rectify the color distortion, the restoration images were equalized by using the deduced histogram equalization. The experiment results showed that the proposed method

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Wang, Jiefei, Yupeng Chen, Tao Li, Jian Lu, and Lixin Shen. "A Residual-Based Kernel Regression Method for Image Denoising." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5245948.

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We propose a residual-based method for denoising images corrupted by Gaussian noise. In the method, by combining bilateral filter and structure adaptive kernel filter together with the use of the image residuals, the noise is suppressed efficiently while the fine features, such as edges, of the images are well preserved. Our experimental results show that, in comparison with several traditional filters and state-of-the-art denoising methods, the proposed method can improve the quality of the restored images significantly.

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Pan, I.-Hui, Ping-Sheng Huang, Te-Jen Chang, and Hsiang-Hsiung Chen. "Multilayer Reversible Information Hiding with Prediction-Error Expansion and Dynamic Threshold Analysis." Sensors 22, no. 13 (2022): 4872. http://dx.doi.org/10.3390/s22134872.

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The rapid development of internet and social media has driven the great requirement for information sharing and intelligent property protection. Therefore, reversible information embedding theory has marked some approaches for information security. Assuming reversibility, the original and embedded data must be completely restored. In this paper, a high-capacity and multilayer reversible information hiding technique for digital images was presented. First, the integer Haar wavelet transform scheme converted the cover image from the spatial into the frequency domain that was used. Furthermore, w

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Kefer, Paul, Fadil Iqbal, Maelle Locatelli, et al. "Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences." Molecular Biology of the Cell 32, no. 9 (2021): 903–14. http://dx.doi.org/10.1091/mbc.e20-11-0689.

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Deep learning offers revolutionary answers to old challenges, in particular for quantitative microscopy. An example is content-aware image restoration that improves the quality of noisy images. A key question is to what extent biological information is restored. Our work addresses this question using particle tracking as an objective metric.

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Lin, Chia-Chen, Yen-Heng Lin, En-Ting Chu, Wei-Liang Tai, and Chun-Jung Lin. "VUF-MIWS: A Visible and User-Friendly Watermarking Scheme for Medical Images." Electronics 14, no. 1 (2024): 122. https://doi.org/10.3390/electronics14010122.

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The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, user-friendly, visible watermarking scheme for medical images—Visual and User-Friendly Medical Image Watermarking Scheme (VUF-MIWS)—designed to secure medical image ownership while maintaining usability for diagnostic purposes. VUF-MIWS employs a unique comb

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Kim, Jinah, Dong Huh, Taekyung Kim, Jaeil Kim, Jeseon Yoo, and Jae-Seol Shim. "Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement." Remote Sensing 12, no. 20 (2020): 3461. http://dx.doi.org/10.3390/rs12203461.

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We propose an unsupervised network with adversarial learning, the Raindrop-aware GAN, which enhances the quality of coastal video images contaminated by raindrops. Raindrop removal from coastal videos faces two main difficulties: converting the degraded image into a clean one by visually removing the raindrops, and restoring the background coastal wave information in the raindrop regions. The components of the proposed network—a generator and a discriminator for adversarial learning—are trained on unpaired images degraded by raindrops and clean images free from raindrops. By creating raindrop

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Ruikar, Dr Sachin D., and Ms Vrushali N. Raut. "A Comparison of Filtering Techniques for Image Quality Improvement in Computed Tomography." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 3 (2013): 670–76. http://dx.doi.org/10.24297/ijct.v7i3.3445.

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Computed Tomography (CT) is an important and most common modality in medical imaging. In CT examinations there is trade off between radiation dose and image quality. If radiation dose is decreased, the noise will unavoidably increase degrading the diagnostic value of the CT image and ifthe radiation dose is increased, the associated risk of cancer also increases especially in paediatric applications. Image filtering techniques perform image pre-processing to improve the quality of images. These techniques serve two major purposes. One is to maintain low radiation dose and another is to make su

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Kinge, Sanjaykumar, B. Sheela Rani, and Mukul Sutaone. "Restored texture segmentation using Markov random fields." Mathematical Biosciences and Engineering 20, no. 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.

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<abstract> <p>Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a hug

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Lyu, Yiwei, Sung Jik Cha, Cheng Jiang, et al. "Step-Calibrated Diffusion for Biomedical Optical Image Restoration." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 5946–54. https://doi.org/10.1609/aaai.v39i6.32635.

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High-quality, high-resolution medical imaging is essential for clinical care. Raman-based biomedical optical imaging uses non-ionizing infrared radiation to evaluate human tissues in real time and is used for early cancer detection, brain tumor diagnosis, and intraoperative tissue analysis. Unfortunately, optical imaging is vulnerable to image degradation due to laser scattering and absorption, which can result in diagnostic errors and misguided treatment. Restoration of optical images is a challenging computer vision task because the sources of image degradation are multi-factorial, stochasti

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Balakin, D. A., A. V. Belinsky, and A. S. Chirkin. "Correlations of Multiplexed Quantum Ghost Images and Improvement of the Quality of Restored Image." Journal of Russian Laser Research 38, no. 2 (2017): 164–72. http://dx.doi.org/10.1007/s10946-017-9630-z.

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Doriguêtto, Paulo Victor Teixeira, Daniela de Almeida, Carolina Oliveira de Lima, Ricardo Tadeu Lopes, and Karina Lopes Devito. "Assessment of marginal gaps and image quality of crowns made of two different restorative materials: An in vitro study using CBCT images." Journal of Dental Research, Dental Clinics, Dental Prospects 16, no. 4 (2022): 243–50. http://dx.doi.org/10.34172/joddd.2022.039.

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Background. The present study assessed the quality of images and the presence of marginal gaps on cone-beam computed tomography (CBCT) images of teeth restored with all-ceramic and metal-ceramic crowns and compared the gap sizes observed on CBCT images with those obtained on micro-CT images. Methods. Thirty teeth restored with metal-ceramic and all-ceramic crowns, properly adapted and with gaps of 0.30 and 0.50 mm, were submitted to micro-CT and CBCT scans. Linear measurements corresponding to the marginal gap (MG) and the absolute marginal discrepancy (AMD) were obtained. The objective assess

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Chang, Chin-Chen, Chih-Yang Lin, and Chun-Sen Tseng. "Secret Image Hiding and Sharing Based on the (t,n)-Threshold." Fundamenta Informaticae 76, no. 4 (2007): 399–411. https://doi.org/10.3233/fun-2007-76401.

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This study proposes a new secret image sharing method based on the (t,n)-threshold. The sharing method is completed through three main steps: (1) quantizing the secret image, (2) generating n shadows from the quantized image, and (3) sharing the n shadows among n cover images. Our goal is both to attain good stego-image and secret image quality preservation effects and to limit the size of each shadow image down to 1/t of that of the secret image for convenient transmission and storage. In the first step, the secret image is divided into non-overlapping 4 × 4 blocks that are quantized accordin

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Saleem, Shahid, Shahbaz Ahmad, and Junseok Kim. "Total Fractional-Order Variation-Based Constraint Image Deblurring Problem." Mathematics 11, no. 13 (2023): 2869. http://dx.doi.org/10.3390/math11132869.

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When deblurring an image, ensuring that the restored intensities are strictly non-negative is crucial. However, current numerical techniques often fail to consistently produce favorable results, leading to negative intensities that contribute to significant dark regions in the restored images. To address this, our study proposes a mathematical model for non-blind image deblurring based on total fractional-order variational principles. Our proposed model not only guarantees strictly positive intensity values but also imposes limits on the intensities within a specified range. By removing negati

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Yue, Ronggang, Humei Wang, Ting Jin, et al. "Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser." Sensors 21, no. 10 (2021): 3309. http://dx.doi.org/10.3390/s21103309.

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Satellites have many high-, medium-, and low-frequency micro vibration sources that lead to the optical axis jitter of the optical load and subsequently degrade the remote sensing image quality. To address this problem, this paper developed an image motion detection and restoration method based on an inertial reference laser, and describe edits principle and key components. To verify the feasibility and performance of this method, this paper also built an image motion measurement and restoration system based on an inertial reference laser, which comprised a camera (including the inertial refer

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Yeh, Chia-Hung, Yu-Wei Lai, Yu-Yang Lin, Mei-Juan Chen, and Chua-Chin Wang. "Underwater Image Enhancement Based on Light Field-Guided Rendering Network." Journal of Marine Science and Engineering 12, no. 7 (2024): 1217. http://dx.doi.org/10.3390/jmse12071217.

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Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwater activities. However, obtaining clear underwater images has been a challenge, because scattering and blur hinder the rendering of true underwater colors, affecting the accuracy of underwater exploration. Therefore, this paper proposes a new deep network model for single underwater image enhancemen

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Prakash, Anju J., and Ferdinand Christopher. "An Enhanced Image Dehazing Method for the Application in Urban Computing." Journal of Computational and Theoretical Nanoscience 16, no. 2 (2019): 793–98. http://dx.doi.org/10.1166/jctn.2019.7811.

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The appearance of images captured in some weather conditions is often degraded due to the presence of haze or fog. These hazy images affect the visibility in the field of computer vision applications, object recognition systems, intelligent transportation, traffic analysis etc. Multi-scale Gradient enhancement is the most popular method used as part of dehazing. But manipulating the image easily turns low dynamic range into high dynamic range images. As a result the restored image become dark or overexposure so that it degrades the quality of the image. As an aim to resolve this problem a bett

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Gong, Jun, Senlin Luo, Wenxin Yu, and Liang Nie. "Inpainting with Separable Mask Update Convolution Network." Sensors 23, no. 15 (2023): 6689. http://dx.doi.org/10.3390/s23156689.

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Image inpainting is an active area of research in image processing that focuses on reconstructing damaged or missing parts of an image. The advent of deep learning has greatly advanced the field of image restoration in recent years. While there are many existing methods that can produce high-quality restoration results, they often struggle when dealing with images that have large missing areas, resulting in blurry and artifact-filled outcomes. This is primarily because of the presence of invalid information in the inpainting region, which interferes with the inpainting process. To tackle this

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Sun, Wei, Jianli Wu, and Haroon Rashid. "Image Enhancement Algorithm of Foggy Sky with Sky based on Sky Segmentation." Journal of Physics: Conference Series 2560, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2560/1/012011.

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Abstract In recent years, image defogging has become a research hotspot in the field of digital image processing. Through defogging enhancement processing, the visual quality of foggy images can be significantly improved, and it is also an important part of subsequent image processing. To overcome the limitations of traditional image dehazing, an image enhancement algorithm for foggy image with sky based on sky segmentation is proposed. Firstly, based on K-means clustering and sky feature analysis, sky region recognition is performed for fog image with sky. Secondly, according to the pixels of

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Nallasivam, Manikandaprabu, Femi D, and Raja Paulsingh J. "Histogram Based Optimized Reversible Steganographic Scheme." ECS Transactions 107, no. 1 (2022): 19089–97. http://dx.doi.org/10.1149/10701.19089ecst.

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A novel prediction-based Reversible Steganographic scheme based on image inpainting can be optimized by choosing the reference pixels using optimization technique. Partial differential equations based on image inpainting are introduced to generate a prediction image, from the reference image that has similar structural and geometric information as the cover image. Then the histogram of the prediction error is shifted to embed the secret bits reversibly using two selected sets of peak points and zero points. From the stego image, the cover image can be restored losslessly after extracting the e

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Alfiarini, Isni Ambar, and Rama Chandra. "Effect of service quality and company image on customer satisfaction mediated by price at CV. Rizqy Abadi Utama Jakarta." Indonesian Journal of Business, Accounting and Management 6, no. 1 (2023): 45–56. https://doi.org/10.36406/ijbam.v6i01.799_rec.

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This research examines the effects of service quality, price, and corporate image on customer satisfaction. It also aims to explore the indirect impact of service quality on customer satisfaction through price and the influence of corporate image on customer satisfaction via price. This study employs an associative research strategy. The population includes clients of CV. Rizqy Abadi Utama, with a sample size of 68 respondents who have used the company's distribution and licensing services. A purposive sampling technique was used, and data was collected through questionnaires, yielding primary

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Alfiarini, Isni Ambar, and Rama Chandra. "Effect of service quality and company image on customer satisfaction mediated by price at CV. Rizqy Abadi Utama Jakarta." Indonesian Journal of Business, Accounting and Management 6, no. 1 (2025): 45–56. https://doi.org/10.36406/ijbam.v6i01.799.149.

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This research examines the effects of service quality, price, and corporate image on customer satisfaction. It also aims to explore the indirect impact of service quality on customer satisfaction through price and the influence of corporate image on customer satisfaction via price. This study employs an associative research strategy. The population includes clients of CV. Rizqy Abadi Utama, with a sample size of 68 respondents who have used the company's distribution and licensing services. A purposive sampling technique was used, and data was collected through questionnaires, yielding primary

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