Neural Block-wise Auto-exposure for Single-Shot High Dynamic Range Imaging

Zhengguo Li; Chaobing Zheng; Wei Wang

cs.CVOct 9, 2026

Neural Block-wise Auto-exposure for Single-Shot High Dynamic Range Imaging

Authors: Zhengguo Li, Chaobing Zheng, Wei Wang

Organizations: Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR), Singapore, 138632 · the school of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China · Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)

Abstract

Block-wise varying exposures are effective for cap- turing appearance of a real-world high dynamic range (HDR) scene via a single-shot. However, brightness order reversal (BOR) artifacts, blocking artifacts and loss of details in highlight/shadow regions are possible issues for block-wise varying exposures. In this paper, a novel neural block-wise auto-exposure algorithm is proposed to address the issues. The proposed algorithm maps a temporary image and its exposure time to the exposure times of all non-overlapping 16 X 16 blocks. The temporary image can be either an sRGB or a raw image. Although the sRGB image is widely adopted in existing auto-exposure algorithms, exposure times of blocks in the highlight and shadow regions might not be accurately learned due to the limited radiance measurement capability of the sRGB image. This paper thus also explores the feasibility of temporary image as a raw image. Due to the better radiance measurement capability of the raw image, the exposure times of blocks are learned more accurately, and more details in the highlight/shadow regions of the real-world HDR scene are captured by the resultant HDR imaging system. Experiment results validate the proposed neural block-wise auto-exposure algorithm.

Figures & tables

Fig. 1 : Diagram of the proposed neural block-wise auto-exposure scheme. Inputs are a temporary image which can be the sRGB image $Z_{N}$ or the raw image $I_{N}$ and its exposure time $\tau_{im}(N)$ , outputs are the exposure times of all non-overlapping $16\times 16$ blocks. The ratios $\tau_{bl}(l)/\tau_{im}(N)$ ’s rather than the exposure times $\tau_{bl}(l)$ ’s are learned by the proposed algorithm.

Fig. 2 : The proposed neural block-wise auto-exposure algorithm is on top of light-weighted U-Net and transformer which has about 0.6 M parameters. Inputs of the transformer are down-sampled channels.

Fig. 3 : Comparison of the proposed algorithm with four quasi-HDR algorithms including DrTMO [ 20 ] , SingleHDR [ 21 ] , DiffHDRSyn [ 19 ] and RAWHDR [ 22 ] as well as four data-driven exposure fusion algorithms including DeepFuse [ 42 ] , MEF-Net [ 38 ] , Difuser [ 43 ] and UltraFusion [ 18 ] on three static HDR scenes. The proposed algorithm captures details in shadow and highlight regions without amplifying noise in shadow regions and producing halo and BOR artifacts.

Fig. 4 : Comparison of the proposed algorithm with four data-driven exposure fusion algorithms including DeepFuse [ 42 ] , MEF-Net [ 38 ] , Difuser [ 43 ] and UltraFusion [ 18 ] on one dynamic HDR scene. The proposed algorithm captures details in shadow and highlight regions without producing halo, BOR, and ghosting artifacts.

Algorithm	DrTMO	SingleHDR	DiffHDRSyn	RAWHDR	DeepFuse	MEF-Net	Difuser	UltraFusion	Proposed
MEF-SSIM $\uparrow$	0.8832	0.8867	0.7265	0.5121	0.8716	0.8704	0.7288	0.9057	0.9238
TMQI $\uparrow$	0.8578	0.8817	0.7927	0.7538	0.8525	0.8215	0.8826	0.9066	0.9204
HyperIQA $\uparrow$	68.03	68.64	60.34	63.22	68.79	69.44	65.02	70.89	70.45
MUSIQ $\uparrow$	59.53	60.50	52.74	51.31	65.87	66.30	49.79	68.81	67.70
NIQE $\downarrow$	2.9742	3.0244	3.4373	2.8552	2.6223	2.5886	3.9263	2.5927	2.5647

TABLE I : Comparison of DrTMO [ 20 ] , SingleHDR [ 21 ] , and DiffHDRSyn [ 19 ] , RAWHDR [ 22 ] , DeepFuse [ 42 ] , MEF-Net [ 38 ] , Difuser [ 43 ] , UltraFusion [ 18 ] and the proposed auto-exposure algorithm using MEF-SSIM, TMQI, HyperIQA and MUSIQ (

\uparrow

larger is better) as well as NIQE (

\downarrow

smaller is better).

Algorithm	Abs Rel $\downarrow$	Sq Rel $\downarrow$	Log RMSE $\downarrow$	SILog $\downarrow$	MEF-SSIM $\uparrow$	TMQI $\uparrow$	HyperIQA $\uparrow$	MUSIQ $\uparrow$	NIQE $\downarrow$
1 (w transformer)	0.1228	0.0023	0.1869	0.1825	0.9238	0.9204	70.45	67.70	2.5647
1 (w/o transformer)	0.1324	0.0026	0.1965	0.1927	0.9230	0.9186	70.18	67.90	2.5654
2	1.6367	0.1202	1.0797	0.3525	0.8243	0.8048	67.26	62.97	3.9211
3	0.3674	0.0051	0.3822	0.3471	0.9198	0.9057	67.26	62.97	3.9211

TABLE II : Comparison of different neural auto-exposure algorithms with temporary image as

I_{N}

using Abs Rel, Se Rel, Log MSE, SILong (

\downarrow

smaller is better), MEF-SSIM [ 44 ] , TMQI [ 45 ] , HyperIQA [ 47 ] , MUSIQ [ 48 ] (

\uparrow

larger is better), and NIEQ [ 46 ] (

\downarrow

smaller is better).

Fig. 5 : Subjective evaluation of three different auto-exposure algorithms with temporary image as raw image $I_{N}$ . The captured images by Algorithm 1 approach the corresponding target images better than Algorithms 2 and 3.

$\alpha$	Abs Rel $\downarrow$	Sq Rel $\downarrow$	Log RMSE $\downarrow$	SILog $\downarrow$	MEF-SSIM $\uparrow$	TMQI $\uparrow$	HyperIQA $\uparrow$	MUSIQ $\uparrow$	NIQE $\downarrow$
0	0.1291	0.0025	0.1933	0.1886	0.7833	0.7563	61.63	57.01	2.6348
0.2	0.1228	0.0023	0.1869	0.1825	0.9238	0.9204	70.45	67.70	2.5647
1	0.7822	0.0617	1.5570	0.1874	0.9229	0.9199	70.43	67.59	2.5692

TABLE III : Comparison of different choices of

\alpha

for the proposed auto-exposure algorithm with the temporary image as

I_{N}

using Abs Rel, Se Rel, Log MSE, SILong (

\downarrow

smaller is better), MEF-SSIM [ 44 ] , TMQI [ 45 ] , HyperIQA [ 47 ] , MUSIQ [ 48 ] (

\uparrow

larger is better), and NIEQ [ 46 ] (

\downarrow

smaller is better)

Temporary image	Abs Rel $\downarrow$	Sq Rel $\downarrow$	Log RMSE $\downarrow$	SILog $\downarrow$	MEF-SSIM $\uparrow$	TMQI $\uparrow$	HyperIQA $\uparrow$	MUSIQ $\uparrow$	NIQE $\downarrow$
$Z_{N}$	0.1408	0.0029	0.2071	0.1976	0.9223	0.9130	70.37	67.57	2.5706
$I_{N}$	0.1228	0.0023	0.1869	0.1825	0.9238	0.9204	70.45	67.70	2.5647

TABLE IV : Comparison of the temporary image as

Z_{N}

and

I_{N}

using Abs Rel, Se Rel, Log MSE, SILong (

\downarrow

smaller is better) as well as captured images using MEF-SSIM [ 44 ] , TMQI [ 45 ] , HyperIQA [ 47 ] , MUSIQ [ 48 ] (

\uparrow

larger is better), and NIEQ [ 46 ] (

\downarrow

smaller is better).

Algorithm	Abs Rel	Sq Rel	Log RMSE	SILog	MEF-SSIM	TMQI	HyperIQA	MUSIQ	NIQE
1	0.1408	0.0029	0.2071	0.1976	0.9223	0.9130	70.37	67.57	2.5706
2	3.5980	0.0806	1.5364	0.5653	0.8398	0.7730	64.99	61.33	3.1980
3	0.2972	0.0066	0.3333	0.3156	0.9188	0.9075	70.11	67.56	2.5721

TABLE V : Comparison of different neural auto-exposure algorithms with the temporary image as

Z_{N}

using Abs Rel, Se Rel, Log MSE, SILong (

\downarrow

smaller is better), MEF-SSIM [ 44 ] , TMQI [ 45 ] , HyperIQA [ 47 ] , MUSIQ [ 48 ] (

\uparrow

larger is better), and NIEQ [ 46 ] (

\downarrow

smaller is better).

Fig. 6 : Training curves of three different algorithms with temporary images as sRGB and raw images, respectively. It is difficult for Algorithm 2 to converge.

Fig. 7 : Subjective comparison of three auto-exposure algorithms with the temporary images as sRGB image $Z_{N}$ . The captured images by Algorithm 1 approach the target images closer than the captured images by Algorithms 2 and 3.

Temporary image	Abs Rel	Sq Rel	Log RMSE	SILog
$Z_{N}$	0.4108	0.0138	0.4121	0.3574
$I_{N}$	0.3813	0.0098	0.3832	0.3406

TABLE VI : Comparison of blocks in the shadow and highlight regions between two choices of the temporary image as

Z_{N}

and

I_{N}

Fig. 8 : Subjective comparison of algorithm 1’s with the temporary images as the raw image $I_{N}$ and the sRGB image $Z_{N}$ , respectively. More details in the highlight regions are captured if the temporary image is the raw image $I_{N}$ due to the better radiance measurement capability of the raw image.

HDR Imaging

High dynamic range imaging captures or reconstructs scene brightness beyond standard images, preserving detail in shadows and highlights.

See all 47 papers

Physics Guided Diffusion Model Based Exposure Interpolation for High Dynamic Range Imaging
Oct 27, 2026Zhengguo Li, Chaobing ZhengHDR Imaging Physics-Informed Diffusion Models
Institute of Advanced Intelligence and Computing, A*STAR, Singapore, 138632 · the school of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
ExpoCM: Exposure-Aware One-Step Generative Single-Image HDR Reconstruction
May 4, 2026Aoyu Liu, Zhen Liu, Ziyi Wang +3Image Enhancement HDR Imaging
University of Electronic Science and Technology of China
High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences
Sep 23, 2026Tao Zhang, Peixian Su, Xingyu Gao +6HDR Imaging HDR Video Reconstruction
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China, and also with Zhejiang Provincial Key Laboratory of Low Altitude Ubiquitous Networking Technology, Hangzhou Dianzi University, Hangzhou, China · Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China, and also with the University of Chinese Academy of Sciences, Beijing, China · School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China +1

Oct 27, 2026J/K move · Enter open · S save

Physics Guided Diffusion Model Based Exposure Interpolation for High Dynamic Range Imaging

Zhengguo Li, Chaobing Zheng

Institute of Advanced Intelligence and Computing, A*STAR, Singapore, 138632 · the school of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China

HDR Imaging Physics-Informed Diffusion Models Diffusion Models for Image Restoration

Open paper AskPDF arXiv

Ghosting artifacts are an open problem for popular multi-shot based high dynamic range (HDR) imaging. An effective solution is to capture two large-exposure-ratio (LER) standard dynamic range (SDR) images simultaneously via a single-shot. Brightness order reversal (BOR) artifacts are generated when the two LER images are directly fused together. It is crucial to interpolate an image with an exposure time in between the exposure times of two LER images. In this paper, a novel physics-guided diffusion model based exposure interpolation algorithm is proposed to address the BOR artifacts. An initial image is interpolated by a physics-driven component, and the residual high-frequency details are generated by a new diffusion model based component. The new diffusion model is based on the correlation analysis of generative models and includes a new predicted target to reduce the variation of the Pearson correlation between the noisy data and the target, as well as the amplification factor of the fitting error. Compared with existing neural augmented exposure interpolation algorithms on top of convolution neural networks, the proposed exposure interpolation algorithm has an inherent advantage to generate more high frequency information. Experiment results validate the proposed exposure interpolation algorithm.

Abstract

HDR Imaging

Explore similar work

Physics Guided Diffusion Model Based Exposure Interpolation for High Dynamic Range Imaging

ExpoCM: Exposure-Aware One-Step Generative Single-Image HDR Reconstruction

High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences