Learning a self-supervised tone mapping operator via feature contrast masking loss

High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning-based methods is limited by the training data. Most existing works use as training set a curated selection of best-performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning-based self-supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics

High-Brightness Image Enhancement Algorithm

In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed method. The experimental objects were license plates. We reconstructed the image in the RGB channel, and gamma correction was carried out. After that, local linear adjustment was completed through a tone mapping window to restore the detailed information of the high-brightness region. The experimental results showed that our algorithm could clearly restore the details of high-brightness local areas. The processed image conformed to the visual effect observed by human eyes but with higher definition. Compared with other algorithms, the proposed algorithm has advantages in terms of both subjective and objective evaluation. It can fully satisfy the needs in various practical applications

Impact of a heterogeneous stator on the rotor-stator interaction-noise: an analytical, experimental and numerical investigation

La présente étude vise à quantifier par une modélisation analytique, des essais et des simulations numériques, l’impact d’un stator hétérogène sur le bruit d’interaction rotor-stator dans les turbomachines axiales. Le travail développé s’appuie sur des premières observations sur un ventilateur axial à basse vitesse à l’École Centrale de Lyon, l’étage LP3. Il a été observé que les deux premières fréquences de passage des pales (FPP) rayonnaient à des niveaux élevés alors qu’elles devaient être coupées par le conduit selon le critère de Tyler & Sofrin. Une campagne expérimentale est alors réalisée sur la configuration de ventilateur hétérogène qui permet la caractérisation des contenus spectral et modal. Afin de s’assurer qu’aucune distorsion d’entrée d’air n’est présente, un écran pour le contrôle de la turbulence est utilisé. Des techniques de décomposition modale sont utilisées sur des antennes pseudo-aléatoires afin d’obtenir les modes acoustiques prédominants. Les résultats montrent un fort rayonnement acoustique des deux premières fréquences de passage des pales et mettent en évidence des modes dominants. La même expérience est ensuite simulée numériquement en utilisant la méthode de Boltzmann sur réseau. Les simulations montrent un bon comportement de la turbomachine mais prédisent une augmentation de pression inférieure à celle de l’expérience. La comparaison entre un stator homogène et hétérogène permet de quantifier directement l’impact de l’hétérogénéité. L’hétérogénéité est alors responsable d’une augmentation du niveau tonal de plus de 10 dB aux deux premières FPP. Le contenu modal mesuré sur la configuration hétérogène est bien retrouvé par les simulations numériques. En outre, l’analyse de l’écoulement dans l’espacement inter-rotor-stator a permis de mettre en évidence l’impact de l’hétérogénéité sur le champ potentiel. Finalement, la modélisation analytique est axée sur deux sources dominantes : le bruit d’interaction de sillages et le bruit d’interaction potentielle. Les résultats montrent une contribution mineure de ce dernier. Les mêmes modes dominants sont retrouvés dans certaines directions de propagation en accord avec ce qui est observé expérimentalement. En dernier lieu, une étude d’optimisation de la position des bras support est présentée. Une des configurations optimales montrant une forte atténuation du niveau de bruit tonal est validée numériquement par des simulations numériques. Les résultats montrent que l’optimisation du positionnement angulaire des aubes structurelles permet d’obtenir une réduction significative des niveaux aux deux premières FPP. L’étude des différentes composantes (analytique, expérimentale et numérique) fournit ainsi une meilleure compréhension des mécanismes de bruit modifiés par l’hétérogénéité du stator.Abstract: The present study aims to quantify by means of analytical modelling, experiments and numerical simulations, the impact of a heterogeneous stator on the rotor-stator noise in axial turbomachines. This study starts with the first observations on an axial low-speed fan at École Centrale de Lyon, the LP3 stage. It has been observed that the first two blade passing frequencies (BPF) were radiating at high levels while they were expected to be cut-off by the duct according to Tyler & Sofrin’s criterion. An experiment is then carried out with the heterogeneous stator configuration which makes it possible to characterize the spectral and modal contents. To ensure that no inflow distortion is present at the inlet, a Turbulence Control Screen is used. Modal decomposition techniques are used with pseudo-random antennas to obtain the predominant acoustic modes. Results show a strong acoustic radiation of the first two BPFs and evidence some dominant modes. The same experiment is then simulated numerically using the lattice Boltzmann method. The simulations show a good physical behaviour of the turbomachine but predict a lower pressure-rise compared with the experiment. The comparison between homogeneous and heterogeneous stators allows quantifying directly the impact of the heterogeneity. The heterogeneity is responsible for a level increase of more than 10 dB at the first two BPFs. The modal content from the numerical simulations on the heterogeneous configuration is also in good agreement with the experiment. In addition, the analysis of the flow in the inter-stage made it possible to highlight the impact of the heterogeneity on the potential field. Finally, the analytical modelling is focused on two dominant sources: wake-interaction noise and potential-interaction noise. Results put in evidence a minor contribution of the latter despite the short rotor-stator spacing. The same dominant modes are found in certain propagation directions in accordance with what is measured in the experiment. Finally, an optimisation of the modified vanes angular position is carried out. One of the optimal configurations showing a great noise attenuation is numerically validated by the LBM. The numerical results show that the optimisation of the azimuthal positioning of the modified vanes makes it possible to obtain a significant reduction of the levels at the first two BPFs. Thereby, the comparison of the analytical, experimental and numerical investigations allows achieving a better understanding of the modification of noise mechanisms caused by the heterogeneity of the stator

Finite element computation of a viscous compressible free shear flow governed by the time dependent Navier-Stokes equations

A finite element algorithm for solution of fluid flow problems characterized by the two-dimensional compressible Navier-Stokes equations was developed. The program is intended for viscous compressible high speed flow; hence, primitive variables are utilized. The physical solution was approximated by trial functions which at a fixed time are piecewise cubic on triangular elements. The Galerkin technique was employed to determine the finite-element model equations. A leapfrog time integration is used for marching asymptotically from initial to steady state, with iterated integrals evaluated by numerical quadratures. The nonsymmetric linear systems of equations governing time transition from step-to-step are solved using a rather economical block iterative triangular decomposition scheme. The concept was applied to the numerical computation of a free shear flow. Numerical results of the finite-element method are in excellent agreement with those obtained from a finite difference solution of the same problem

Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline

Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-exposed regions caused by quantization and saturation of camera sensors. In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We then propose to learn three specialized CNNs to reverse these steps. By decomposing the problem into specific sub-tasks, we impose effective physical constraints to facilitate the training of individual sub-networks. Finally, we jointly fine-tune the entire model end-to-end to reduce error accumulation. With extensive quantitative and qualitative experiments on diverse image datasets, we demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.Comment: CVPR 2020. Project page: https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR Code: https://github.com/alex04072000/SingleHD

4K-Resolution Photo Exposure Correction at 125 FPS with ~8K Parameters

The illumination of improperly exposed photographs has been widely corrected using deep convolutional neural networks or Transformers. Despite with promising performance, these methods usually suffer from large parameter amounts and heavy computational FLOPs on high-resolution photographs. In this paper, we propose extremely light-weight (with only ~8K parameters) Multi-Scale Linear Transformation (MSLT) networks under the multi-layer perception architecture, which can process 4K-resolution sRGB images at 125 Frame-Per-Second (FPS) by a Titan RTX GPU. Specifically, the proposed MSLT networks first decompose an input image into high and low frequency layers by Laplacian pyramid techniques, and then sequentially correct different layers by pixel-adaptive linear transformation, which is implemented by efficient bilateral grid learning or 1x1 convolutions. Experiments on two benchmark datasets demonstrate the efficiency of our MSLTs against the state-of-the-arts on photo exposure correction. Extensive ablation studies validate the effectiveness of our contributions. The code is available at https://github.com/Zhou-Yijie/MSLTNet.Comment: WACV202

High-fidelity colour reproduction for high-dynamic-range imaging

The aim of this thesis is to develop a colour reproduction system for high-dynamic-range (HDR) imaging. Classical colour reproduction systems fail to reproduce HDR images because current characterisation methods and colour appearance models fail to cover the dynamic range of luminance present in HDR images. HDR tone-mapping algorithms have been developed to reproduce HDR images on low-dynamic-range media such as LCD displays. However, most of these models have only considered luminance compression from a photographic point of view and have not explicitly taken into account colour appearance. Motivated by the idea to bridge the gap between crossmedia colour reproduction and HDR imaging, this thesis investigates the fundamentals and the infrastructure of cross-media colour reproduction. It restructures cross-media colour reproduction with respect to HDR imaging, and develops a novel cross-media colour reproduction system for HDR imaging. First, our HDR characterisation method enables us to measure HDR radiance values to a high accuracy that rivals spectroradiometers. Second, our colour appearance model enables us to predict human colour perception under high luminance levels. We first built a high-luminance display in order to establish a controllable high-luminance viewing environment. We conducted a psychophysical experiment on this display device to measure perceptual colour attributes. A novel numerical model for colour appearance was derived from our experimental data, which covers the full working range of the human visual system. Our appearance model predicts colour and luminance attributes under high luminance levels. In particular, our model predicts perceived lightness and colourfulness to a significantly higher accuracy than other appearance models. Finally, a complete colour reproduction pipeline is proposed using our novel HDR characterisation and colour appearance models. Results indicate that our reproduction system outperforms other reproduction methods with statistical significance. Our colour reproduction system provides high-fidelity colour reproduction for HDR imaging, and successfully bridges the gap between cross-media colour reproduction and HDR imaging