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There is rising interest in differentiable rendering, which allows explicitly modeling geometric priors and constraints in optimization pipelines using first-order methods such as backpropagation. Incorporating such domain knowledge can…

图像与视频处理 · 电气工程与系统科学 2023-08-09 Michael Wilmanski , Jonathan Tamir

Acquired images for medical and other purposes can be affected by noise from both the equipment used in the capturing or the environment. This can have adverse effect on the information therein. Thus, the need to restore the image to its…

图像与视频处理 · 电气工程与系统科学 2024-01-19 E. G. Onyedinma , I. E. Onyenwe

Raster images can have a range of various distortions connected to their raster structure. Upsampling them might in effect substantially yield the raster structure of the original image, known as aliasing. The upsampling itself may…

计算机视觉与模式识别 · 计算机科学 2007-05-23 Artur Rataj

Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to manage products, favoring their widespread diffusion and…

图像与视频处理 · 电气工程与系统科学 2022-04-05 Edoardo Daniele Cannas , Nicolò Bonettini , Sara Mandelli , Paolo Bestagini , Stefano Tubaro

Interferometric Synthetic Aperture Radar (InSAR) Imaging methods are usually based on algorithms of match-filtering type, without considering the scene's characteristic, which causes limited imaging quality. Besides, post-processing steps…

信号处理 · 电气工程与系统科学 2022-10-07 Xu Zhan , Xiaoling Zhang , Shunjun Wei , Jun Shi

Speckle suppression in synthetic aperture radar (SAR) images is a key processing step which continues to be a research topic. A wide variety of methods, using either spatially-based approaches or transform-based strategies, have been…

计算机视觉与模式识别 · 计算机科学 2024-08-30 Alejandro Mestre-Quereda , Juan M. Lopez-Sanchez

In Synthetic Aperture Radar (SAR) imaging, despeckling is very important for image analysis,whereas speckle is known as a kind of multiplicative noise caused by the coherent imaging system. During the past three decades, various algorithms…

图像与视频处理 · 电气工程与系统科学 2020-12-01 Qianqian Zhang , Ruizhi Sun

In this paper we propose a measure of anisotropy as a quality parameter to estimate the amount of noise in noisy images. The anisotropy of an image can be determined through a directional measure, using an appropriate statistical…

计算机视觉与模式识别 · 计算机科学 2011-06-30 Salvador Gabarda , Gabriel Cristobal

In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The applicability of these filter sets is demonstrated by evaluating their performance in two contrasting…

图像与视频处理 · 电气工程与系统科学 2018-11-26 Mohit Prabhushankar , Dogancan Temel , Ghassan AlRegib

Conventional LIDAR systems require hundreds or thousands of photon detections to form accurate depth and reflectivity images. Recent photon-efficient computational imaging methods are remarkably effective with only 1.0 to 3.0 detected…

应用统计 · 统计学 2019-11-13 Joshua Rapp , Vivek K Goyal

The aim of this paper is twofold: In the first part, we leverage recent results on scenario design to develop randomized algorithmsfor approximating the image set of a nonlinear mapping, that is, a (possibly noisy) mapping of a set via a…

最优化与控制 · 数学 2015-07-30 Fabrizio Dabbene , Didier Henrion , Constantino Lagoa , Pavel Shcherbakov

Deep unfolding networks have recently emerged as a promising approach for synthetic aperture radar (SAR) imaging. However, baseline unfolding networks, typically derived from iterative reconstruction algorithms such as the alternating…

信号处理 · 电气工程与系统科学 2025-06-27 Shiping Fu , Yufan Chen , Zhe Zhang , Xiaolan Qiu , Qixiang Ye

Synthetic Aperture Radar (SAR) provides all-weather, high-resolution imaging capabilities, but its unique imaging mechanism often requires expert interpretation, limiting its widespread applicability. Translating SAR images into more easily…

计算机视觉与模式识别 · 计算机科学 2024-07-09 Xinyu Bai , Feng Xu

Noise, an unwanted component in an image, can be the reason for the degradation of Image at the time of transmission or capturing. Noise reduction from images is still a challenging task. Digital Image Processing is a component of Digital…

图像与视频处理 · 电气工程与系统科学 2024-10-31 Sahil Ali Akbar , Ananya Verma

Advancements in Sonar image capture have enabled researchers to apply sophisticated object identification algorithms in order to locate targets of interest in images such as mines. Despite progress in this field, modern sonar automatic…

计算机视觉与模式识别 · 计算机科学 2016-01-05 John McKay , Raghu Raj , Vishal Monga , Jason Isaacs

Noise removal from images is a part of image restoration in which we try to reconstruct or recover an image that has been degraded by using apriori knowledge of the degradation phenomenon. Noises present in images can be of various types…

计算机视觉与模式识别 · 计算机科学 2014-12-03 Poorna Banerjee Dasgupta

The bilateral filter is a useful nonlinear filter which without smoothing edges, it does spatial averaging. In the literature, the effectiveness of this method for image denoising is shown. In this paper, an extension of this method is…

计算机视觉与模式识别 · 计算机科学 2017-02-07 Seyede Mahya Hazavei , Hamid Reza Shahdoosti

The reconstructed images from the Synthetic Aperture Radar (SAR) data suffer from multiplicative noise as well as low contrast level. These two factors impact the quality of the SAR images significantly and prevent any attempt to extract…

图像与视频处理 · 电气工程与系统科学 2024-12-05 Shahrokh Hamidi

This work presents a comprehensive examination of the use of information theory for understanding Polarimetric Synthetic Aperture Radar (PolSAR) images by means of contrast measures that can be used as test statistics. Due to the phenomenon…

统计方法学 · 统计学 2014-02-11 A. C. Frery , A. D. C. Nascimento , R. J. Cintra

Despite the advantages of all-weather and all-day high-resolution imaging, synthetic aperture radar (SAR) images are much less viewed and used by general people because human vision is not adapted to microwave scattering phenomenon.…

计算机视觉与模式识别 · 计算机科学 2019-10-10 Shilei Fu , Feng Xu , Ya-Qiu Jin