Deep learning is commonly used to reconstruct HDR images from LDR images. LDR
stack-based methods are used for single-image HDR reconstruction, generating an
HDR image from a deep learning-generated LDR stack. However, current methods
generate the stack with predetermined exposure values (EVs), which may limit
the quality of HDR reconstruction. To address this, we propose the continuous
exposure value representation (CEVR), which uses an implicit function to
generate LDR images with arbitrary EVs, including those unseen during training.
Our approach generates a continuous stack with more images containing diverse
EVs, significantly improving HDR reconstruction. We use a cycle training
strategy to supervise the model in generating continuous EV LDR images without
corresponding ground truths. Our CEVR model outperforms existing methods, as
demonstrated by experimental results.

使用连续曝光值表示 (CVR) 的深度学习模型可从低动态范围图像 (LDR) 重建高动态范围图像 (HDR)，通过生成包含各种不同曝光值图像的连续堆栈，该模型在 HDR 重建方面优于现有方法。

学习连续曝光值表示用于单幅图像高动态范围重建

Learning Continuous Exposure Value Representations for Single-Image HDR  Reconstruction

Due to limited camera capacities, digital images usually have a narrower
dynamic illumination range than real-world scene radiance. To resolve this
problem, High Dynamic Range (HDR) reconstruction is proposed to recover the
dynamic range to better represent real-world scenes. However, due to different
physical imaging parameters, the tone-mapping functions between images and real
radiance are highly diverse, which makes HDR reconstruction extremely
challenging. Existing solutions can not explicitly clarify a corresponding
relationship between the tone-mapping function and the generated HDR image, but
this relationship is vital when guiding the reconstruction of HDR images. To
address this problem, we propose a method to explicitly estimate the tone
mapping function and its corresponding HDR image in one network. Firstly, based
on the characteristics of the tone mapping function, we construct a model by a
polynomial to describe the trend of the tone curve. To fit this curve, we use a
learnable network to estimate the coefficients of the polynomial. This curve
will be automatically adjusted according to the tone space of the Low Dynamic
Range (LDR) image, and reconstruct the real HDR image. Besides, since all
current datasets do not provide the corresponding relationship between the tone
mapping function and the LDR image, we construct a new dataset with both
synthetic and real images. Extensive experiments show that our method
generalizes well under different tone-mapping functions and achieves SOTA
performance.

提出了一种在一个网络中明确估算色调映射函数和其对应的高动态范围（HDR）图像的方法，通过使用多项式描述色调曲线的趋势并利用可学习网络估算多项式系数，实现对真实 HDR 图像的重建；同时构建了一个包含合成和真实图像的新数据集来解决现有数据集中色调映射函数和中低动态范围（LDR）图像之间的对应关系缺乏问题，并在各种色调映射函数下取得了最先进的性能。

通过深度显式多项式曲线估计实现高动态范围图像重建

High Dynamic Range Image Reconstruction via Deep Explicit Polynomial  Curve Estimation

It is very challenging to reconstruct a high dynamic range (HDR) from a low
dynamic range (LDR) image as an ill-posed problem. This paper proposes a
luminance attentive network named LANet for HDR reconstruction from a single
LDR image. Our method is based on two fundamental observations: (1) HDR images
stored in relative luminance are scale-invariant, which means the HDR images
will hold the same information when multiplied by any positive real number.
Based on this observation, we propose a novel normalization method called " HDR
calibration " for HDR images stored in relative luminance, calibrating HDR
images into a similar luminance scale according to the LDR images. (2) The main
difference between HDR images and LDR images is in under-/over-exposed areas,
especially those highlighted. Following this observation, we propose a
luminance attention module with a two-stream structure for LANet to pay more
attention to the under-/over-exposed areas. In addition, we propose an extended
network called panoLANet for HDR panorama reconstruction from an LDR panorama
and build a dualnet structure for panoLANet to solve the distortion problem
caused by the equirectangular panorama. Extensive experiments show that our
proposed approach LANet can reconstruct visually convincing HDR images and
demonstrate its superiority over state-of-the-art approaches in terms of all
metrics in inverse tone mapping. The image-based lighting application with our
proposed panoLANet also demonstrates that our method can simulate natural scene
lighting using only LDR panorama. Our source code is available at
this https URL

本文提出了一种基于 LANet 的亮度注意网络，用于从单个 LDR 图像重建 HDR 图像，并提出了一种新的 HDR 校准方法和亮度注意模块来解决 HDR 和 LDR 图像之间的不同之处，同时我们也扩展了网络以进行全景图重建，该方法在 HDR 重建和全景图方面优于现有方法且代码可用。