HDR Reconstruction Boosting with Training-Free and Exposure-Consistent Diffusion

TL;DR

This work tackles the problem of reconstructing HDR images in scenes with severely over-exposed regions where information is lost. It proposes a training-free, diffusion-based HDR boosting pipeline that uses diffusion priors for inpainting, guided by ControlNet conditioning and SDEdit refinement, coupled with a compensation stage to enforce cross-exposure luminance consistency. The method seamlessly augments existing indirect and direct HDR reconstruction approaches without additional training, and demonstrates improvements across standard HDR datasets on perceptual and non-reference metrics, while maintaining alignment across multiple exposures. The approach offers practical benefits for enhancing HDR pipelines in-the-wild, with extensibility to handle under-exposed regions in future work.

Abstract

Single LDR to HDR reconstruction remains challenging for over-exposed regions where traditional methods often fail due to complete information loss. We present a training-free approach that enhances existing indirect and direct HDR reconstruction methods through diffusion-based inpainting. Our method combines text-guided diffusion models with SDEdit refinement to generate plausible content in over-exposed areas while maintaining consistency across multi-exposure LDR images. Unlike previous approaches requiring extensive training, our method seamlessly integrates with existing HDR reconstruction techniques through an iterative compensation mechanism that ensures luminance coherence across multiple exposures. We demonstrate significant improvements in both perceptual quality and quantitative metrics on standard HDR datasets and in-the-wild captures. Results show that our method effectively recovers natural details in challenging scenarios while preserving the advantages of existing HDR reconstruction pipelines. Project page: https://github.com/EusdenLin/HDR-Reconstruction-Boosting

Figures (10)

• Figure 1: Overview of our training-free HDR reconstruction pipeline. Given an input LDR image (EV0), we generate bracketed LDR images using an existing HDR reconstruction method. Our iterative pipeline then enhances these results through (1) an inpainting stage guided by exposure and condition maps, (2) HDR merging and alignment of the generated content, and (3) a compensation stage to ensure physical consistency. The top-right inset shows the progressive refinement of the over-exposed regions through our pipeline stages. The upper sky region in the aligned case shows the clear alignment across EV images. The lower sky region in the compensation and refined cases demonstrates our algorithm's ability to hallucinate realistic over-expose regions with plausible intensity and texture. The upper part of the sky region in the aligned case shows the clear alignment across EV images. The lower parts of the sky region in the compensation and refined cases show the ability of our algorithm to hallucinate realistic over-expose regions with reasonable intensity and texture.
• Figure 2: Limitations arising from naively combining indirect HDR reconstruction methods and over-exposed regions inpainting. Independent inpainting of each EV bracket, without cross-EV alignment, can introduce ghosting artifacts in the merged HDR result. These artifacts stem from inconsistencies in the generated content, becoming apparent after merging the independently inpainted exposures using Debevec's method.
• Figure 3: Motivation for luminance compensation in HDR reconstruction. The top row shows the existence of inpainted intensities below the lower bound of over-exposed regions may lead to unreasonable inverse CRF estimation and misaligned results. The bottom row demonstrates our intensity compensation approach, which ensures bounded intensities, resulting in proper inverse CRF estimation and exposure-aligned reconstruction that matches the input LDR.
• Figure 4: Inpainting pipeline in our method. The depth-conditioned inpainting pipeline with scheduled strength is able to generate reasonable and consistent content across different EVs and iterations. The scheduled SDEdit strength enables us to balance detail preservation with creative generation. In the early iterations, we allow the model to explore a broader range of plausible scene details, while in later iterations, we retain the refined details from previous steps and only update pixels that do not meet the physical constraints.
• Figure 5: Compensation pipeline in our method. Our pipeline ensures proper exposure relationships through iterative refinement. (Top) Overview of how inpainted LDRs are combined with luminance residuals to produce compensated results after tone-mapping. (Bottom) Visualization over four iterations shows inpainted results, decreasing residuals, and shrinking mask regions. The process maintains proper luminance lower bounds while focusing refinement on problematic areas through selective masking, preventing CRF estimation issues.