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 lightweight CNN-based algorithm for extreme dual-exposure image fusion, which achieves better functionality than a conventional DCNN and can be deployed in embedded systems. Two sub-networks are utilized: a GlobalNet (G) and a DetailNet (D). The goal of G is to learn the global illumination information on the spatial dimension, whereas D aims to enhance local details on the channel dimension. Both G and D are based solely on depthwise convolution (D_Conv) and pointwise convolution (P_Conv) to reduce required parameters and computations. Experimental results show that this proposed technique could generate HDR images in extremely exposed regions with sufficient details to be legible. Our model outperforms other state-of-the-art approaches in peak signal-to-noise ratio (PSNR) score by 0.9 to 8.7 while achieving 16.7 to 306.2 times parameter reduction.
Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex
Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；ETcTI, University Politehnica Timisoara（蒂米șoara工业大学ETcTI）；West University of Timisoara（蒂米șoara西大学）
The University of Tokyo（东京大学）；Shanghai AI Laboratory（上海人工智能实验室）；RIKEN AIP（理化学研究所人工智能研究中心）；Shanghai Jiao Tong University（上海交通大学）；Tencent Youtu Lab（腾讯优图实验室）
Challenging illumination conditions (low-light, under-exposure and over-exposure) in the real world not only cast an unpleasant visual appearance but also taint the computer vision tasks. After camera captures the raw-RGB data, it renders standard sRGB images with image signal processor (ISP). By decomposing ISP pipeline into local and global image components, we propose a lightweight fast Illumination Adaptive Transformer (IAT) to restore the normal lit sRGB image from either low-light or under/over-exposure conditions. Specifically, IAT uses attention queries to represent and adjust the ISP-…
A Lightweight Global-Target Framework for Multi-Domain No-Reference Image Quality Assessment in UAV Imagery
Department of Mechatronic Engineering, Institute for Basic Sciences, Technology and Innovation, Pan African University (PAUSTI)（机械电子工程系，基础科学、技术与创新研究所，泛非大学）；Department of Mechatronic Engineering, Jomo Kenyatta University of Agriculture and Technology (JKUAT)（机械电子工程系，乔莫·肯雅塔农业与技术大学）；College of Science and Technology, University of Rwanda（科学与技术学院，卢旺达大学）
Lensless imaging seeks to replace/remove the lens in a conventional imaging system. The earliest cameras were in fact lensless, relying on long exposure times to form images on the other end of a small aperture in a darkened room/container (camera obscura). The introduction of a lens allowed for more light throughput and therefore shorter exposure times, while retaining sharp focus. The incorporation of digital sensors readily enabled the use of computational imaging techniques to post-process and enhance raw images (e.g. via deblurring, inpainting, denoising, sharpening). Recently, imaging sc…
Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；College of Surveying and Geoinformatics, Tongji University（同济大学测绘与地理信息学院）；School of Computer and Information, Hefei University of Technology（合肥工业大学计算机与信息学院）
Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices
Key Laboratory of Precision and Intelligent Chemistry, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China（精密与智能化学重点实验室，精密机械与精密仪器系，中国科学技术大学）；School of Information and Communication Engineering, University of Electronic Science and Technology of China（信息与通信工程学院，电子科学与技术大学）
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Photo retouching aims at improving the aesthetic visual quality of images that suffer from photographic defects, especially for poor contrast, over/under exposure, and inharmonious saturation. In practice, photo retouching can be accomplished by a series of image processing operations. As most commonly-used retouching operations are pixel-independent, i.e., the manipulation on one pixel is uncorrelated with its neighboring pixels, we can take advantage of this property and design a specialized algorithm for efficient global photo retouching. We analyze these global operations and find that the…
JADE-GS: Joint Allocation of Deblurring Evidence for Event-Assisted 3D Gaussian Splatting
School of Mechatronic Engineering and Automation, Shanghai University（上海大学机电工程与自动化学院）；Shanghai Baoshan Shangda General Intelligent Robotics Research Institute（上海宝山尚大通用智能机器人研究院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
SnapPix: Efficient-Coding--Inspired In-Sensor Compression for Edge Vision
University of Rochester（罗切斯特大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；Washington University in St. Louis（圣路易斯华盛顿大学）；Shanghai Jiao Tong University（上海交通大学）；Northeastern University（东北大学）
