The goal of high dynamic range (HDR) imaging is to estimate potential high-quality images
from multi-exposed low dynamic range (LDR) inputs. Intuitively, there exist various possible …

Abstract Map** Low Dynamic Range (LDR) images with different exposures to High
Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to …

Multi-frame depth estimation generally achieves high accuracy relying on the multi-view
geometric consistency. When applied in dynamic scenes, eg, autonomous driving, this …

Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has
recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs …

In many computer vision applications (eg, robotics and autonomous driving), high dynamic
range (HDR) data is necessary for object detection algorithms to handle a variety of lighting …

Photographs captured by smartphones and mid-range cameras have limited spatial
resolution and dynamic range, with noisy response in underexposed regions and color …

Single image based HDR reconstruction methods using deep neural network have been
proposed to mainly restore the lost details in the overexposed region. However they cannot …

Multi-frame high dynamic range (HDR) reconstruction methods try to expand the range of
illuminance with differently exposed images. They suffer from ghost artifacts when camera …

Although image quality assessment (IQA) in-the-wild has been researched in computer
vision, it is still challenging to precisely estimate perceptual image quality in the presence of …

Abstract Reconstructing High Dynamic Range (HDR) video from image sequences captured
with alternating exposures is challenging especially in the presence of large camera or …