Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Structured light scanning has gained widespread applications in industrial metrology;
however, measuring objects that have large surface-reflectivity variations is challenging. For …

The advancement of new digital image sensors has enabled the design of exposure
multiplexing schemes where a single image capture can have multiple exposures and …

We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-
Bracket HDR Imaging. In order to address the challenges of fusing multiple brackets from …

The high dynamic range (HDR) image recovery from the low dynamic range (LDR) image
aims to estimate HDR image by decompressing luminance range and enhancing details of …

Inverse tone mapping technique is widely used to restore the lost textures from a single low
dynamic range image. Recently, many stack‐based deep inverse tone mapping networks …

In this paper, we propose a multi-objective camera ISP framework that utilizes Deep
Reinforcement Learning (DRL) and camera ISP toolbox that consist of network-based and …

Hardware image signal processing (ISP), aiming at converting RAW inputs to RGB images,
consists of a series of processing blocks, each with multiple parameters. Traditionally, ISP …

摘要为提升真实场景视觉信号的采集质量, 往往需要通过多种融合方式获取相应的图像, 例如,
多聚焦, 多曝光, 多光谱和多模态等. 针对视觉信号采集的以上特性, 图像融合技术旨在利用同一 …

With limited dynamic range in consumer-level photographs and electronic displays, high
dynamic range images can be rendered as the standard dynamic range image by tone …