FlexHDR: Modeling Alignment and Exposure Uncertainties for Flexible HDR Imaging

Catley-Chandar, Sibi; Tanay, Thomas; Lucas, Vandroux,; Leonardis, Aleš; Slabaugh, Greg; Pérez-Pellitero, Eduardo

doi:10.1109/tip.2022.3203562

Cited by 13 publications

(2 citation statements)

References 38 publications

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“…A self-supervised method is proposed in [ 34 ], where a set of three bracketed–exposed LDR images was used to create HDR patches for self-supervision based on static and well-exposed areas of the image. FlexHDR [ 35 ] takes an arbitrary number of LDR images as input and computes the optical flow between these differently exposed images using a flow network. After flow estimation, the FlexHDR model addresses uncertainties caused by exposure and alignment via an attention network, and the final HDR is generated by a multi-stage fusion-based merging network.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Light Polarization for Deep HDR Imaging from a Single Exposure

Pistellato

Fatima

Wimmer

2023

Sensors

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In computational photography, high dynamic range (HDR) imaging refers to the family of techniques used to recover a wider range of intensity values compared to the limited range provided by standard sensors. Classical techniques consist of acquiring a scene-varying exposure to compensate for saturated and underexposed regions, followed by a non-linear compression of intensity values called tone mapping. Recently, there has been a growing interest in estimating HDR images from a single exposure. Some methods exploit data-driven models trained to estimate values outside the camera’s visible intensity levels. Others make use of polarimetric cameras to reconstruct HDR information without exposure bracketing. In this paper, we present a novel HDR reconstruction method that employs a single PFA (polarimetric filter array) camera with an additional external polarizer to increase the scene’s dynamic range across the acquired channels and to mimic different exposures. Our contribution consists of a pipeline that effectively combines standard HDR algorithms based on bracketing and data-driven solutions designed to work with polarimetric images. In this regard, we present a novel CNN (convolutional neural network) model that exploits the underlying mosaiced pattern of the PFA in combination with the external polarizer to estimate the original scene properties, and a second model designed to further improve the final tone mapping step. The combination of such techniques enables us to take advantage of the light attenuation given by the filters while producing an accurate reconstruction. We present an extensive experimental section in which we validate the proposed method on both synthetic and real-world datasets specifically acquired for the task. Quantitative and qualitative results show the effectiveness of the approach when compared to state-of-the-art methods. In particular, our technique exhibits a PSNR (peak signal-to-noise ratio) on the whole test set equal to 23 dB, which is 18% better with respect to the second-best alternative.

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Section: Related Workmentioning

confidence: 99%

Exploiting Light Polarization for Deep HDR Imaging from a Single Exposure

Pistellato

Fatima

Wimmer

2023

Sensors

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“…The HDR representation offers an unlimited tonal range and prioritizes the preservation of fine details. It allows for a more immersive visual experience when viewing movies, photographs, playing computer games, or inspecting visualizations [1,2]. However, most consumer display devices are not equipped to handle such rich visual content.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Tone Mapping Model for High Dynamic Range Imaging

Mehmood,

Shi,

Khan

et al. 2023

IEEE Access

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One of the key challenges in tone mapping is to preserve the perceptual quality of high dynamic range (HDR) images when mapping them to standard dynamic range (SDR) displays. Traditional tone mapping operators (TMOs) compress the luminance of HDR images without considering the surround and display conditions emanating into suboptimal results. Current research addresses this challenge by incorporating perceptual color appearance attributes. In this work, we propose a TMO (TMOz) that leverages CIECAM16 perceptual attributes, i.e., brightness, colorfulness, and hue. TMOz accounts for the effects of both the surround and the display conditions to achieve more optimal colorfulness reproduction. The perceptual brightness is compressed, and the perceptual color scales, i.e., colorfulness and hue are derived from HDR images by employing CIECAM16 color adaptation equations. A psychophysical experiment was conducted to automate the brightness compression parameter. The model employs fully automatic and adaptive approach, obviating the requirement for manual parameter selection. TMOz was evaluated in terms of contrast, colorfulness and overall image quality. The objective and subjective evaluation methods revealed that the proposed model outperformed the state-of-the-art TMOs.INDEX TERMS CIECAM16, high dynamic range, TMOz, tone mapping, perceptual tone mapping

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