Robust estimation of exposure ratios in multi-exposure image stacks

Merging multi-exposure image stacks into a high dynamic range (HDR) image requires knowledge of accurate exposure times. When exposure times are inaccurate, for example, when they are extracted from a camera's EXIF metadata, the reconstructed HDR images reveal banding artifacts at smooth gradients. To remedy this, we propose to estimate exposure ratios directly from the input images. We derive the exposure time estimation as an optimization problem, in which pixels are selected from pairs of exposures to minimize estimation error caused by camera noise. When pixel values are represented in the logarithmic domain, the problem can be solved efficiently using a linear solver. We demonstrate that the estimation can be easily made robust to pixel misalignment caused by camera or object motion by collecting pixels from multiple spatial tiles. The proposed automatic exposure estimation and alignment eliminates banding artifacts in popular datasets and is essential for applications that require physically accurate reconstructions, such as measuring the modulation transfer function of a display. The code for the method is available.Comment: 11 pages, 11 figures, journa

Noise-Aware Merging of High Dynamic Range Image Stacks Without Camera Calibration

A near-optimal reconstruction of the radiance of a High Dynamic Range scene from an exposure stack can be obtained by modeling the camera noise distribution. The latent radiance is then estimated using Maximum Likelihood Estimation. But this requires a well-calibrated noise model of the camera, which is difficult to obtain in practice. We show that an unbiased estimation of comparable variance can be obtained with a simpler Poisson noise estimator, which does not require the knowledge of camera-specific noise parameters. We demonstrate this empirically for four different cameras, ranging from a smartphone camera to a full-frame mirrorless camera. Our experimental results are consistent for simulated as well as real images, and across different camera settings

Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping

Many image enhancement or editing operations, such as forward and inverse tone mapping or color grading, do not have a unique solution, but instead a range of solutions, each representing a different style. Despite this, existing learning-based methods attempt to learn a unique mapping, disregarding this style. In this work, we show that information about the style can be distilled from collections of image pairs and encoded into a 2- or 3-dimensional vector. This gives us not only an efficient representation but also an interpretable latent space for editing the image style. We represent the global color mapping between a pair of images as a custom normalizing flow, conditioned on a polynomial basis of the pixel color. We show that such a network is more effective than PCA or VAE at encoding image style in low-dimensional space and lets us obtain an accuracy close to 40 dB, which is about 7-10 dB improvement over the state-of-the-art methods.Comment: Published in European Conference on Visual Media Production (CVMP '22

HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content

High-Dynamic-Range Visual-Difference-Predictor version 3, or HDR-VDP-3, is a visual metric that can fulfill several tasks, such as full-reference image/video quality assessment, prediction of visual differences between a pair of images, or prediction of contrast distortions. Here we present a high-level overview of the metric, position it with respect to related work, explain the main differences compared to version 2.2, and describe how the metric was adapted for the HDR Video Quality Measurement Grand Challenge 2023

Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views

Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured images. This success has led to many variants of the techniques, each evaluated on a set of test views typically using image quality metrics such as PSNR, SSIM, or LPIPS. There has been a lack of research on how NVS methods perform with respect to perceived video quality. We present the first study on perceptual evaluation of NVS and NeRF variants. For this study, we collected two datasets of scenes captured in a controlled lab environment as well as in-the-wild. In contrast to existing datasets, these scenes come with reference video sequences, allowing us to test for temporal artifacts and subtle distortions that are easily overlooked when viewing only static images. We measured the quality of videos synthesized by several NVS methods in a well-controlled perceptual quality assessment experiment as well as with many existing state-of-the-art image/video quality metrics. We present a detailed analysis of the results and recommendations for dataset and metric selection for NVS evaluation

Neural Fields with Hard Constraints of Arbitrary Differential Order

While deep learning techniques have become extremely popular for solving a broad range of optimization problems, methods to enforce hard constraints during optimization, particularly on deep neural networks, remain underdeveloped. Inspired by the rich literature on meshless interpolation and its extension to spectral collocation methods in scientific computing, we develop a series of approaches for enforcing hard constraints on neural fields, which we refer to as Constrained Neural Fields (CNF). The constraints can be specified as a linear operator applied to the neural field and its derivatives. We also design specific model representations and training strategies for problems where standard models may encounter difficulties, such as conditioning of the system, memory consumption, and capacity of the network when being constrained. Our approaches are demonstrated in a wide range of real-world applications. Additionally, we develop a framework that enables highly efficient model and constraint specification, which can be readily applied to any downstream task where hard constraints need to be explicitly satisfied during optimization.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Comparison of single image HDR reconstruction methods — the caveats of quality assessment

As the problem of reconstructing high dynamic range (HDR) images from a single exposure has attracted much research effort, it is essential to provide a robust protocol and clear guidelines on how to evaluate and compare new methods. In this work, we compared six recent single image HDR reconstruction (SI-HDR) methods in a subjective image quality experiment on an HDR display. We found that only two methods produced results that are, on average, more preferred than the unprocessed single exposure images. When the same methods are evaluated using image quality metrics, as typically done in papers, the metric predictions correlate poorly with subjective quality scores. The main reason is a significant tone and color difference between the reference and reconstructed HDR images. To improve the predictions of image quality metrics, we propose correcting for the inaccuracies of the estimated camera response curve before computing quality values. We further analyze the sources of prediction noise when evaluating SI-HDR methods and demonstrate that existing metrics can reliably predict only large quality differences

Dataset for HDR4CV: High dynamic range dataset with adversarial illumination for testing computer vision methods

This dataset is intended to test the robustness of computer vision methods under challenging illumination conditions. The dataset includes four scenes (*Faces*, *Tunnel*, *Street-parallel*, *Street-diagonal*), which were captured multiple times, each time under a different illumination condition (**night**, **hdr**, **glare**, and **uniform**). A non-challenging **uniform** illumination condition serves as a reference and can be used to generate labels for the other three challenging illumination conditions and test a given computer vision method. This lets us avoid the tedious task of manually labeling the data. Refer to README.md for further details