A small ISO and a small exposure time are usually used to capture an image in the back or low light conditions which results in an image with negligible motion blur and small noise but look dark. In this paper, a single image brightening algorithm is introduced to brighten such an image. The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times. The virtual images are first generated via intensity mapping functions (IMFs) which are computed using camera response functions (CRFs) and this is a model-driven approach. Both the virtual images are then enhanced by using a data-driven approach, i.e. a residual convolutional neural network to approach the ground truth images. The model-driven approach and the data-driven one compensate each other in the proposed hybrid learning framework. The final brightened image is obtained by fusing the original image and two virtual images via a multi-scale exposure fusion algorithm with properly defined weights. Experimental results show that the proposed brightening algorithm outperforms existing algorithms in terms of the MEF-SSIM metric.
Institute of Applied Computing and Community Code (IAC3)（应用计算与社区代码研究所（IAC3））；Universitat de les Illes Balears（巴利阿里群岛大学）
Multi-exposure fusion (MEF) is a technique for combining different images of the same scene acquired with different exposure settings into a single image. All the proposed MEF algorithms combine the set of images, somehow choosing from each one the part with better exposure. We propose a novel multi-exposure image fusion chain taking into account noise removal. The novel method takes advantage of DCT processing and the multi-image nature of the MEF problem. We propose a joint fusion and denoising strategy taking advantage of spatio-temporal patch selection and collaborative 3D thresholding. Th…
Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to multi-exposure image fusion. However, although much efforts have been made on developing MEF algorithms, the lack of benchmark makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus significantly hindering the development of this field. In this paper, we fill this gap by proposing a benchmark for multi-exposure image fusion (MEFB) wh…
Combining images with different exposure settings are of prime importance in the field of computational photography. Both transform domain approach and filtering based approaches are possible for fusing multiple exposure images, to obtain the well-exposed image. We propose a Discrete Cosine Transform (DCT-based) approach for fusing multiple exposure images. The input image stack is processed in the transform domain by an averaging operation and the inverse transform is performed on the averaged image obtained to generate the fusion of multiple exposure image. The experimental observation leads…
FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimation
Nankai University（南开大学）；Beihang University（北京航空航天大学）；vivo Mobile Communication Co. Ltd.（维沃移动通信有限公司）；Guangdong University of Petrochemical Technology（广东石油化工学院）
University of South Carolina（南卡罗来纳大学）；Nanyang Technological University（南洋理工大学）；Alibaba Group（阿里巴巴集团）；Cleveland State University（克利夫兰州立大学）；Tianjin University（天津大学）
Shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful state-of-the-art deep neural networks could hardly recover traceless shadow-removed background. This paper proposes a new solution for this task by formulating it as an exposure fusion problem to address the challenges. Intuitively, we can first estimate multiple over-exposure images w.r.t. the input image to let the shadow regions in these images have the same color with shadow-free areas in the input image. Then, we …
Chandigarh Engineering College（昌迪加尔工程学院）；Thapar Institute of Engineering & Technology, University（塔帕尔工程技术大学）
Recent innovations shows that blending of details captured by single Low Dynamic Range (LDR) sensor overcomes the limitations of standard digital cameras to capture details from high dynamic range scene. We present a method to produce well-exposed fused image that can be displayed directly on conventional display devices. The ambition is to preserve details in poorly illuminated and brightly illuminated regions. Proposed approach does not require true radiance reconstruction and tone manipulation steps. The aforesaid objective is achieved by taking into account local information measure that s…
Digital Medical Research Center, School of Basic Medical Science, Fudan University（复旦大学基础医学院数字医学研究中心）；Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention（上海市医学图像计算与计算机辅助干预重点实验室）
In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-supervised reconstruction tasks according to the characteristics of multi-exposure images and conduct these tasks simultaneously using multi-task learning; through this process, the network can learn the characteristics of multi-exposure images and extract more generalized features…
High dynamic range (HDR) imaging enables to immortalize natural scenes similar to the way that they are perceived by human observers. With regular low dynamic range (LDR) capture/display devices, significant details may not be preserved in images due to the huge dynamic range of natural scenes. To minimize the information loss and produce high quality HDR-like images for LDR screens, this study proposes an efficient multi-exposure fusion (MEF) approach with a simple yet effective weight extraction method relying on principal component analysis, adaptive well-exposedness and saliency maps. Thes…
Schulich School of Engineering, University of Calgary（卡尔加里大学舒立克工程学院）；School of Engineering, Westlake University（西湖大学工程学院）
Deep convolutional neural networks (DCNNs) have aided high dynamic range (HDR) imaging recently and have received a lot of attention. The quality of DCNN-generated HDR images has overperformed the traditional counterparts. However, DCNNs are prone to be computationally intensive and power-hungry, and hence cannot be implemented on various embedded computing platforms with limited power and hardware resources. Embedded systems have a huge market, and utilizing DCNNs' powerful functionality into them will further reduce human intervention. To address the challenge, we propose LightFuse, a lightw…
Image resolution is an important criterion for many applications based on satellite imagery. In this work, we adapt a state-of-the-art kernel regression technique for smartphone camera burst super-resolution to satellites. This technique leverages the local structure of the image to optimally steer the fusion kernels, limiting blur in the final high-resolution prediction, denoising the image, and recovering details up to a zoom factor of 2. We extend this approach to the multi-exposure case to predict from a sequence of multi-exposure low-resolution frames a high-resolution and noise-free one.…
Hong Kong Baptist University（香港浸会大学）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究院）；vivo Mobile Communication Co. Ltd（维沃移动通信有限公司）；City University of Hong Kong（香港城市大学）；Nanyang Technological University（南洋理工大学）
High Dynamic Range (HDR) imaging via multi-exposure fusion is an important task for most modern imaging platforms. In spite of recent developments in both hardware and algorithm innovations, challenges remain over content association ambiguities caused by saturation, motion, and various artifacts introduced during multi-exposure fusion such as ghosting, noise, and blur. In this work, we propose an Attention-guided Progressive Neural Texture Fusion (APNT-Fusion) HDR restoration model which aims to address these issues within one framework. An efficient two-stream structure is proposed which sep…
Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction
School of Biomedical Engineering and Instrument Science, Zhejiang University（浙江大学生物医学工程与仪器科学学院）；School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
Unsupervised Learning Based Multi-Scale Exposure Fusion
school of Information Science and Engineering, Wuhan University of Science and Technology（武汉科技大学信息科学与工程学院）；Institute for Infocomm Research, A*STAR（新加坡资讯通信研究院（新加坡科技研究局下属机构））
Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means. In this work, we propose a super-resolution method for such multi-exposure sequences, a problem that has received very little attention in the literature. The proposed method can handle the signal-dependent noise in the inputs, process sequences of any length, and be robust to inaccuracies in the exposure times. Furthermore, it can be trained end-to-end with self-supervision, without requiring ground truth high resolution frames, which makes it especially s…
Low-light imaging with handheld mobile devices is a challenging issue. Limited by the existing models and training data, most existing methods cannot be effectively applied in real scenarios. In this paper, we propose a new low-light image restoration method by using the complementary information of short- and long-exposure images. We first propose a novel data generation method to synthesize realistic short- and longexposure raw images by simulating the imaging pipeline in lowlight environment. Then, we design a new long-short-exposure fusion network (LSFNet) to deal with the problems of low-…
NeuroKoop: Neural Koopman Fusion of Structural-Functional Connectomes for Identifying Prenatal Drug Exposure in Adolescents
Department of Computer Science, Georgia State University（计算机科学系，佐治亚州立大学）；Neuroscience Institute, Georgia State University（神经科学研究所，佐治亚州立大学）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（跨机构神经影像与数据科学转化研究中心（TReNDS））；Georgia State University, Georgia Institute of Technology, and Emory University（佐治亚州立大学、佐治亚理工学院和埃默里大学）
Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion
College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
EPOFusion: Exposure aware Progressive Optimization Method for Infrared and Visible Image Fusion
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；College of Information Science and Technology, Zhejiang Shuren University（浙江树人大学信息科技学院）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电机电子工程系）
Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancement
College of Aerospace Science and Engineering, National University of Defense Technology（航天科学与工程学院，国防科技大学）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）；Beijing Institute of Tracking and Telecommunication Technology（北京跟踪与电信技术研究所）；National Key Laboratory of Space Integrated Information System（空间一体化信息系统国家重点实验室）
Duke University（杜克大学）；University of Arizona（亚利桑那大学）
We demonstrate a physics-aware transformer for feature-based data fusion from cameras with diverse resolution, color spaces, focal planes, focal lengths, and exposure. We also demonstrate a scalable solution for synthetic training data generation for the transformer using open-source computer graphics software. We demonstrate image synthesis on arrays with diverse spectral responses, instantaneous field of view and frame rate.
Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion
School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）；DUT-RU International School of Information Science & Engineering, Dalian University of Technology（大连理工大学盘锦国际信息科学与工程学院）
Anisotropic Diffusion for Details Enhancement in Multi-Exposure Image Fusion
Baddi University of Emerging Sciences and Technology（巴迪新兴科学与技术大学）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Jaypee University of Information Technology（贾佩信息技术大学）
There and Back Again: A Flexible-Frame Transformer for Multi-Exposure Fusion
Nankai International Advanced Research Institute (SHENZHEN·FUTIAN)（南开国际先进研究院（深圳·福田））；Peng Cheng Laboratory（鹏城实验室）；College of Computer Science, Nankai University（南开大学计算机学院）；The Hong Kong Polytechnic University（香港理工大学）；OPPO Research Institute（OPPO研究院）
ExpoMotion: A Large-Scale Benchmark and A Householder Projection Network for Multi-Exposure Fusion
Nankai International Advanced Research Institute (SHENZHEN·FUTIAN)（南开大学国际研究院（深圳·福田））；Pengcheng Laboratory（鹏城实验室）；College of Computer Science, Nankai University（南开大学计算机科学学院）；The Hong Kong Polytechnic University（香港理工大学）；OPPO Research Institute（OPPO研究院）
LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；Hangzhou Institue of Technology, Xidian University（西安电子科技大学杭州学院）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；School of Information Science and Engineering, Dalian University of Technology（大连理工大学信息科学与工程学院）
In current practice, scene survey is carried out by workers using total stations. The method has high accuracy, but it incurs high costs if continuous monitoring is needed. Techniques based on photogrammetry, with the relatively cheaper digital cameras, have gained wide applications in many fields. Besides point measurement, photogrammetry can also create a three-dimensional (3D) model of the scene. Accurate 3D model reconstruction depends on high quality images. Degraded images will result in large errors in the reconstructed 3D model. In this paper, we propose a method that can be used to im…
Qualcomm India（高通印度）；Indian Institute of Technology Madras（马德拉斯印度理工学院）；Mad Street Den
Learning-based methods have enabled the recovery of a video sequence from a single motion-blurred image or a single coded exposure image. Recovering video from a single motion-blurred image is a very ill-posed problem and the recovered video usually has many artifacts. In addition to this, the direction of motion is lost and it results in motion ambiguity. However, it has the advantage of fully preserving the information in the static parts of the scene. The traditional coded exposure framework is better-posed but it only samples a fraction of the space-time volume, which is at best 50% of the…
