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Stable imaging in adverse environments (e.g., total darkness) makes thermal infrared (TIR) cameras a prevalent option for night scene perception. However, the low contrast and lack of chromaticity of TIR images are detrimental to human…

计算机视觉与模式识别 · 计算机科学 2023-10-25 Fu-Ya Luo , Shu-Lin Liu , Yi-Jun Cao , Kai-Fu Yang , Chang-Yong Xie , Yong Liu , Yong-Jie Li

Nighttime thermal infrared (NTIR) image colorization, also known as translation of NTIR images into daytime color images (NTIR2DC), is a promising research direction to facilitate nighttime scene perception for humans and intelligent…

计算机视觉与模式识别 · 计算机科学 2022-08-08 Fu-Ya Luo , Yi-Jun Cao , Kai-Fu Yang , Yong-Jie Li

In real-world environments, outdoor imaging systems are often affected by disturbances such as rain degradation. Especially, in nighttime driving scenes, insufficient and uneven lighting shrouds the scenes in darkness, resulting degradation…

计算机视觉与模式识别 · 计算机科学 2024-04-09 Cidan Shi , Lihuang Fang , Han Wu , Xiaoyu Xian , Yukai Shi , Liang Lin

Image based rendering is a fundamental problem in computer vision and graphics. Modern techniques often rely on depth image for the 3D construction. However for most of the existing depth cameras, the large and unpredictable noises can be…

计算机视觉与模式识别 · 计算机科学 2016-02-17 Rashi Chaudhary , Himanshu Dasgupta

Today, most methods for image understanding tasks rely on feed-forward neural networks. While this approach has allowed for empirical accuracy, efficiency, and task adaptation via fine-tuning, it also comes with fundamental disadvantages.…

计算机视觉与模式识别 · 计算机科学 2024-04-19 Julian Ost , Tanushree Banerjee , Mario Bijelic , Felix Heide

We propose a simple method for estimating noise level from a single color image. In most image-denoising algorithms, an accurate noise-level estimate results in good denoising performance; however, it is difficult to estimate noise level…

计算机视觉与模式识别 · 计算机科学 2019-04-05 Akihiro Nakamura , Michihiro Kobayashi

Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasant images to the human eye than other methods, yet, they may…

图像与视频处理 · 电气工程与系统科学 2022-04-28 Marcos V. Conde , Maxime Burchi , Radu Timofte

Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light…

计算机视觉与模式识别 · 计算机科学 2021-04-08 Jun Liu , Ryan Wen Liu , Jianing Sun , Tieyong Zeng

Images captured in weak illumination conditions could seriously degrade the image quality. Solving a series of degradation of low-light images can effectively improve the visual quality of images and the performance of high-level visual…

计算机视觉与模式识别 · 计算机科学 2022-12-07 Jiang Hai , Zhu Xuan , Songchen Han , Ren Yang , Yutong Hao , Fengzhu Zou , Fang Lin

Depth perception is paramount to tackle real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image represents the most versatile solution, since a standard camera is…

计算机视觉与模式识别 · 计算机科学 2020-06-11 Filippo Aleotti , Giulio Zaccaroni , Luca Bartolomei , Matteo Poggi , Fabio Tosi , Stefano Mattoccia

Modelling the mapping from scene irradiance to image intensity is essential for many computer vision tasks. Such mapping is known as the camera response. Most digital cameras use a nonlinear function to map irradiance, as measured by the…

计算机视觉与模式识别 · 计算机科学 2022-09-09 Yunfeng Zhao , Stuart Ferguson , Huiyu Zhou , Karen Rafferty

Existing methods for enhancing dark images captured in a very low-light environment assume that the intensity level of the optimal output image is known and already included in the training set. However, this assumption often does not hold,…

图像与视频处理 · 电气工程与系统科学 2023-04-05 Evgeny Hershkovitch Neiterman , Michael Klyuchka , Gil Ben-Artzi

The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technologies that can classify and detect objects in images…

One of the major challenges in the field of computer vision especially for detection, segmentation, recognition, monitoring, and automated solutions, is the quality of images. Image degradation, often caused by factors such as rain, fog,…

计算机视觉与模式识别 · 计算机科学 2025-05-20 Muhammad Awais Amin , Adama Ilboudo , Abdul Samad bin Shahid , Amjad Ali , Waqas Haider Khan Bangyal

Non-photorealistic rendering techniques work on image features and often manipulate a set of characteristics such as edges and texture to achieve a desired depiction of the scene. Most computational photography methods decompose an image…

计算机视觉与模式识别 · 计算机科学 2016-04-20 Akshay Gadi Patil , Shanmuganathan Raman

Images captured under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction. These factors can significantly hinder the…

计算机视觉与模式识别 · 计算机科学 2023-05-16 Miao Zhang , Yiqing Shen , Shenghui Zhong

The paper describes a new image processing for a non-photorealistic rendering. The algorithm is based on a random generation of gray tones and competing statistical requirements. The gray tone value of each pixel in the starting image is…

图形学 · 计算机科学 2007-05-23 A. Sparavigna , B. Montrucchio

Content watermarking is an important tool for the authentication and copyright protection of digital media. However, it is unclear whether existing watermarks are robust against adversarial attacks. We present the winning solution to the…

计算机视觉与模式识别 · 计算机科学 2025-08-29 Fahad Shamshad , Tameem Bakr , Yahia Shaaban , Noor Hussein , Karthik Nandakumar , Nils Lukas

Recently, image enhancement and restoration have become important applications on mobile devices, such as super-resolution and image deblurring. However, most state-of-the-art networks present extremely high computational complexity. This…

We present surface normal estimation using a single near infrared (NIR) image. We are focusing on fine-scale surface geometry captured with an uncalibrated light source. To tackle this ill-posed problem, we adopt a generative adversarial…

计算机视觉与模式识别 · 计算机科学 2016-03-25 Youngjin Yoon , Gyeongmin Choe , Namil Kim , Joon-Young Lee , In So Kweon
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