A Deeper Analysis of Volumetric Relightable Faces.

Portrait viewpoint and illumination editing is an important problem with several applications in VR/AR, movies, and photography. Comprehensive knowledge of geometry and illumination is critical for obtaining photorealistic results. Current methods are unable to explicitly model in 3D while handling both viewpoint and illumination editing from a single image. In this paper, we propose VoRF, a novel approach that can take even a single portrait image as input and relight human heads under novel illuminations that can be viewed from arbitrary viewpoints. VoRF represents a human head as a continuous volumetric field and learns a prior model of human heads using a coordinate-based MLP with individual latent spaces for identity and illumination. The prior model is learned in an auto-decoder manner over a diverse class of head shapes and appearances, allowing VoRF to generalize to novel test identities from a single input image. Additionally, VoRF has a reflectance MLP that uses the intermediate features of the prior model for rendering One-Light-at-A-Time (OLAT) images under novel views. We synthesize novel illuminations by combining these OLAT images with target environment maps. Qualitative and quantitative evaluations demonstrate the effectiveness of VoRF for relighting and novel view synthesis, even when applied to unseen subjects under uncontrolled illumination. This work is an extension of Rao et al. (VoRF: Volumetric Relightable Faces 2022). We provide extensive evaluation and ablative studies of our model and also provide an application, where any face can be relighted using textual input.

Response mechanism of ecosystem gross primary productivity to cloud and aerosol changes in a Chinese winter-wheat cropland.

Changes in clouds and aerosols may alter the quantity of solar radiance and its diffuse components, as well as air temperature (Ta) and vapor pressure deficit (VPD), thereby affecting canopy photosynthesis. Our aim was to determine how ecosystem gross primary productivity (GPP) responds to the cloudiness and aerosol depth changes, as indicated by diffuse light fraction (fDIF). The environmental factors that caused these responses were examined using 2 years of eddy covariance data from a winter-wheat cropland in northern China. The GPP decreased significantly along with the fDIF in a nonlinear pattern, with a determination coefficient of 0.91. Changes in fDIF altered total photosynthetic active radiation (PAR), diffuse PAR, Ta and VPD. The variations in GPP with fDIF in both fDIF change Phase I (fDIF < 0.65) and Phase II (fDIF > 0.65) resulted from the combined effects of multiple environmental factors. Because the driving factors were closely correlated, a path analysis was used to distinguish their respective contribution to the GPP response to fDIF by integrating path coefficients. In Phases I and II, the decreased responses of GPP to fDIF were mainly caused by total PAR and diffuse PAR, respectively, which contributed approximately 49% and 37% to GPP variations, respectively. Our research has certain implications for the necessity to consider fDIF and to incorporate diffuse light into photosynthetic models.

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations.

The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O-B) differences can significantly deviate from the Gaussian distribution assumed in the variational data assimilation theory. In this study, we introduce two cloud-affected (Ca) indices to quantify the impact of cloud amount and establish dynamic observation error models to address biases between O-B and Gaussian distributions when assimilating all-sky data from FY-4A/AGRI observations. For each Ca index, we evaluate two dynamic observation error models: a two-segment and a three-segment linear model. Our findings indicate that the three-segment linear model we propose better conforms to the statistical characteristics of FY-4A/AGRI observations and improves the Gaussianity of the O-B probability density function. Dynamic observation error models developed in this study are capable of handling cloud-free or cloud-affected FY-4A/AGRI observations in a uniform manner without cloud detection.

Res-NeuS: Deep Residuals and Neural Implicit Surface Learning for Multi-View Reconstruction.

Surface reconstruction using neural networks has proven effective in reconstructing dense 3D surfaces through image-based neural rendering. Nevertheless, current methods are challenging when dealing with the intricate details of large-scale scenes. The high-fidelity reconstruction performance of neural rendering is constrained by the view sparsity and structural complexity of such scenes. In this paper, we present Res-NeuS, a method combining ResNet-50 and neural surface rendering for dense 3D reconstruction. Specifically, we present appearance embeddings: ResNet-50 is used to extract the appearance depth features of an image to further capture more scene details. We interpolate points near the surface and optimize their weights for the accurate localization of 3D surfaces. We introduce photometric consistency and geometric constraints to optimize 3D surfaces and eliminate geometric ambiguity existing in current methods. Finally, we design a 3D geometry automatic sampling to filter out uninteresting areas and reconstruct complex surface details in a coarse-to-fine manner. Comprehensive experiments demonstrate Res-NeuS's superior capability in the reconstruction of 3D surfaces in complex, large-scale scenes, and the harmful distance of the reconstructed 3D model is 0.4 times that of general neural rendering 3D reconstruction methods and 0.6 times that of traditional 3D reconstruction methods.

Enhancing View Synthesis with Depth-Guided Neural Radiance Fields and Improved Depth Completion.

Neural radiance fields (NeRFs) leverage a neural representation to encode scenes, obtaining photorealistic rendering of novel views. However, NeRF has notable limitations. A significant drawback is that it does not capture surface geometry and only renders the object surface colors. Furthermore, the training of NeRF is exceedingly time-consuming. We propose Depth-NeRF as a solution to these issues. Specifically, our approach employs a fast depth completion algorithm to denoise and complete the depth maps generated by RGB-D cameras. These improved depth maps guide the sampling points of NeRF to be distributed closer to the scene's surface, benefiting from dense depth information. Furthermore, we have optimized the network structure of NeRF and integrated depth information to constrain the optimization process, ensuring that the termination distribution of the ray is consistent with the scene's geometry. Compared to NeRF, our method accelerates the training speed by 18%, and the rendered images achieve a higher PSNR than those obtained by mainstream methods. Additionally, there is a significant reduction in RMSE between the rendered scene depth and the ground truth depth, which indicates that our method can better capture the geometric information of the scene. With these improvements, we can train the NeRF model more efficiently and achieve more accurate rendering results.

The Adaption of Recent New Concepts in Neural Radiance Fields and Their Role for High-Fidelity Volume Reconstruction in Medical Images.

Volume reconstruction techniques are gaining increasing interest in medical domains due to their potential to learn complex 3D structural information from sparse 2D images. Recently, neural radiance fields (NeRF), which implicitly model continuous radiance fields based on multi-layer perceptrons to enable volume reconstruction of objects at arbitrary resolution, have gained traction in natural image volume reconstruction. However, the direct application of NeRF to medical volume reconstruction presents unique challenges due to differences in imaging principles, internal structure requirements, and boundary delineation. In this paper, we evaluate different NeRF techniques developed for natural images, including sampling strategies, feature encoding, and the use of complimentary features, by applying them to medical images. We evaluate three state-of-the-art NeRF techniques on four datasets of medical images of different complexity. Our goal is to identify the strengths, limitations, and future directions for integrating NeRF into the medical domain.

Impact of Multi-Scattered LiDAR Returns in Fog.

In the context of autonomous driving, the augmentation of existing data through simulations provides an elegant solution to the challenge of capturing the full range of adverse weather conditions in training datasets. However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment. Consequently, in this work, Monte Carlo simulations are employed to assess the relevance of multiple-scattered light to the detected LiDAR signal in different types of fog, with scattering phase functions calculated from Mie theory considering real particle size distributions. Bidirectional path tracing is used within the self-developed GPU-accelerated Monte Carlo software to compensate for the unfavorable photon statistics associated with the limited detection aperture of the LiDAR geometry. To validate the Monte Carlo software, an analytical solution of the radiative transfer equation for the time-resolved radiance in terms of scattering orders is derived, thereby providing an explicit representation of the double-scattered contributions. The results of the simulations demonstrate that the shape of the detected signal can be significantly impacted by multiple-scattered light, depending on LiDAR geometry and visibility. In particular, double-scattered light can dominate the overall signal at low visibilities. This indicates that considering higher scattering orders is essential for improving AI-based perception models.

Holographic Brain Theory: Super-Radiance, Memory Capacity and Control Theory.

We investigate Quantum Electrodynamics corresponding to the holographic brain theory introduced by Pribram to describe memory in the human brain. First, we derive a super-radiance solution in Quantum Electrodynamics with non-relativistic charged bosons (a model of molecular conformational states of water) for coherent light sources of holograms. Next, we estimate memory capacity of a brain neocortex, and adopt binary holograms to manipulate optical information. Finally, we introduce a control theory to manipulate holograms involving biological water's molecular conformational states. We show how a desired waveform in holography is achieved in a hierarchical model using numerical simulations.

Light Penetrating the Seawater Column as the Indicator of Oil Suspension-Monte Carlo Modelling for the Case of the Southern Baltic Sea.

The strong need to control investments related to oil extraction and the growing demand for offshore deep-water exploration are the reasons for looking for tools to make up a global underwater monitoring system. Therefore, the current study analyses the possibility of revealing the existence of oil-in-water emulsions in the water column, based on the modelling of the downwelling radiance detected by a virtual underwater sensor. Based on the Monte Carlo simulation for the large numbers of solar photons in the water, the analyses were carried out for eight wavelengths ranging from 412 to 676 nm using dispersed oil with a concentration of 10 ppm. The optical properties of the seawater were defined as typical for the southern Baltic Sea, while the oil emulsion model was based on the optical properties of crude oil extracted in this area. Based on the above-mentioned assumptions and modelling, a spectral index was obtained, with the most favourable combination of 555/412 nm, whose value is indicative of the presence of an oil emulsion in the water.

Spectroradiometer Calibration for Radiance Transfer Measurements.

Optical remote sensing and Earth observation instruments rely on precise radiometric calibrations which are generally provided by the broadband emission from large-aperture integrating spheres. The link between the integrating sphere radiance and an SI-traceable radiance standard is made by spectroradiometer measurements. In this work, the calibration efforts of a Spectra Vista Corporation (SVC) HR-1024i spectroradiometer are presented to study how these enable radiance transfer measurements at the Calibration Home Base (CHB) for imaging spectrometers at the Remote Sensing Technology Institute (IMF) of the German Aerospace Center (DLR). The spectral and radiometric response calibrations of an SVC HR-1024i spectroradiometer are reported, as well as the measurements of non-linearity and its sensitivity to temperature changes and polarized light. This achieves radiance transfer measurements with the calibrated spectroradiometer with relative expanded uncertainties between 1% and 3% (k=2) over the wavelength range of 380 nm to 2500 nm, which are limited by the uncertainties of the applied radiance standard.

Evaluation of the Health Status of Indonesian Watersheds Using Impervious Surface Area as an Indicator.

Impervious surfaces affect the ecosystem function of watersheds. Therefore, the impervious surface area percentage (ISA%) in watersheds has been regarded as an important indicator for assessing the health status of watersheds. However, accurate and frequent estimation of ISA% from satellite data remains a challenge, especially at large scales (national, regional, or global). In this study, we first developed a method to estimate ISA% by combining daytime and nighttime satellite data. We then used the developed method to generate an annual ISA% distribution map from 2003 to 2021 for Indonesia. Third, we used these ISA% distribution maps to assess the health status of Indonesian watersheds according to Schueler's criteria. Accuracy assessment results show that the developed method performed well from low ISA% (rural) to high ISA% (urban) values, with a root mean square difference value of 0.52 km2, a mean absolute percentage difference value of 16.2%, and a bias of -0.08 km2. In addition, since the developed method uses only satellite data as input, it can be easily implemented in other regions with some modifications according to differences in light use efficiency and economic development in each region. We also found that 88% of Indonesian watersheds remain without impact in 2021, indicating that the health status of Indonesian watersheds is not a serious problem. Nevertheless, Indonesia's total ISA increased significantly from 3687.4 km2 in 2003 to 10,505.5 km2 in 2021, and most of the increased ISA was in rural areas. These results indicate that negative trends in health status in Indonesian watersheds may emerge in the future without proper watershed management.

Study for skin gloss perception using computer modulation and frequency separation on surface reflection.

OBJECTIVE: Skin gloss is a parameter of beauty and health and is used to evaluate the efficacy of makeup or skin care products. Skin gloss is a perception-based characteristic, which yet needs to be evaluated quantitatively and objectively. This study aimed to investigate the factors affecting the perception of matte, glossiness and greasiness on Korean women's skin, focusing on the effect of surface reflection. METHODS: In this study, a visual evaluation of skin gloss was performed using modulated images that combined facial reference (normal) images with images characterized by 64 different levels of surface reflection to investigate factors that might affect the perception of matte, glossy and greasy skin. Fifteen participants classified the respective images into three categories (matte, glossy and greasy), and the intensity of each image was rated using a three-point scale (weak, moderate or strong). A multinomial logistic regression analysis was performed using 960 data points (n = 64 × 15). A high-frequency average + 1 standard deviation (SD = 4) was set as the criterion for a highly glossy object. RESULTS: A significant difference was found between the three categories considering four parameters: low-frequency mean (LFM) and SD (LFSD) and high-frequency mean (HFM) and SD (HFSD). The results of the multinomial logistic regression analysis showed that LFM and HFSD were significant influencing factors for perceiving matte, glossiness and greasiness. As the LFM and HFSD of the images increased, perception changed from matte, to glossy and greasy. Further, the total number and total area of highly glossy objects were significantly different among the three categories. CONCLUSION: We identified four different factors related to perceiving matte, glossiness and greasiness on the faces of Korean women, including the LFM and HFSD of surface reflection, as well as the number of highly glossy objects and the area of surface reflection. Furthermore, we identified evenness of shine as a key factor for the perception of greasiness. These parameters could be useful for distinguishing skin gloss and for a quantitative evaluation of greasiness.

OBJECTIF: L'éclat de la peau est un paramètre de beauté et de santé, et est utilisé pour évaluer l'efficacité des produits de maquillage ou de soins de la peau. L'éclat de la peau est une caractéristique basée sur la perception, qui doit pourtant être évaluée quantitativement et objectivement. Cette étude visait à examiner les facteurs affectant la perception de la matité, de l'éclat et du caractère graisseux sur la peau de femmes coréennes, en se concentrant sur l'effet de la réflexion de surface. MÉTHODES: Dans cette étude, une évaluation visuelle de l'éclat de la peau a été réalisée à l'aide d'images modulées combinant des images de référence du visage (normales) et des images caractérisées par 64 niveaux différents de réflexion de surface afin d'étudier les facteurs susceptibles d'affecter la perception d'une peau mate, brillante et grasse. Quinze participants ont classé les images respectives en trois catégories (peau mate, brillante et grasse), et l'intensité de chaque image a été évaluée sur une échelle de trois points (faible, modérée ou forte). Une analyse de régression logistique multinomiale a été réalisée à partir de 960 points de données (n = 64 x 15). Une moyenne de haute fréquence + 1 écart type (ET = 4) a été définie comme critère pour un objet très brillant. RÉSULTATS: Une différence significative a été constatée entre les trois catégories en tenant compte de quatre paramètres : la moyenne des basses fréquences (MBF) et l'ET (ETBF), et la moyenne des hautes fréquences (MHF) et l'ET (ETHF). Les résultats de l'analyse de régression logistique multinomiale ont montré que la MBF et l'ETHF étaient des facteurs d'influence significatifs pour percevoir la matité, l'éclat et le caractère graisseux. Au fur et à mesure que la MBF et l'ETHF des images augmentaient, la perception passait de « mate ¼ à « brillante ¼ et « grasse ¼. En outre, le nombre total et la surface totale des objets très brillants étaient significativement différents dans les trois catégories. CONCLUSION: Nous avons identifié quatre facteurs différents liés à la perception de la matité, de l'éclat et du caractère graisseux sur le visage de femmes coréennes, notamment la MBF et l'ETHF de la réflexion de surface, ainsi que le nombre d'objets très brillants et la zone de la réflexion de surface. En outre, nous avons identifié l'uniformité de l'éclat comme un facteur clé de la perception du caractère graisseux. Ces paramètres pourraient être utiles pour distinguer l'éclat de la peau et pour une évaluation quantitative du caractère graisseux.

Design of a Compact Transfer Radiometer for a Radiometric Benchmark Transfer Chain.

In order to meet the high-accuracy calibration requirements of satellite remote sensing instruments, a transfer radiometer for an on-orbit radiometric benchmark transfer chain has been developed, which provides vital technical support for realizing the radiometric calibration uncertainty of the order of 10-3 for remote sensing instruments. The primary role of the transfer radiometer is to convert from the spectral power responsivity traceable to a cryogenic radiometer to the spectral radiance responsivity and transfer it to the imaging spectrometer. At a wavelength of 852.1 nm, the combined uncertainty of the radiance measurement comparison experiment between the transfer radiometer and a radiance meter is 0.43% (k = 1), and the relative deviation of the measurements between the transfer radiometer and the radiance meter is better than 0.36%, which is better than the combined uncertainty of the radiance measurement comparison experiment. This demonstrates that the transfer radiometer can achieve radiance measurements with a measurement uncertainty better than 0.3% (k = 1).

Initial Performance Analysis of the At-Altitude Radiance Ratio Method for Reflectance Conversion of Hyperspectral Remote Sensing Data.

In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene is an essential component of many analysis tasks. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability and production of accurate reflectance measurements. However, the at-altitude radiance ratio (AARR), a more recently proposed methodology, is attractive as it allows reflectance conversion to be carried out in real time throughout data collection, does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions. The benefits of AARR can substantially reduce the level of effort required for collection setup and subsequent data analysis, and provide a means for large-scale automation of remote sensing data collection, even in atypical flight conditions. In this study, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from hyperspectral radiance data under a variety of challenging illumination conditions. The observed error introduced by AARR is often on par with ELM and acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Additionally, a number of radiometric and atmospheric corrections are proposed that could increase the accuracy of the method in future trials, warranting further research.

Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy.

Current interest in Fourier lightfield microscopy is increasing, due to its ability to acquire 3D images of thick dynamic samples. This technique is based on simultaneously capturing, in a single shot, and with a monocular setup, a number of orthographic perspective views of 3D microscopic samples. An essential feature of Fourier lightfield microscopy is that the number of acquired views is low, due to the trade-off relationship existing between the number of views and their corresponding lateral resolution. Therefore, it is important to have a tool for the generation of a high number of synthesized view images, without compromising their lateral resolution. In this context we investigate here the use of a neural radiance field view synthesis method, originally developed for its use with macroscopic scenes acquired with a moving (or an array of static) digital camera(s), for its application to the images acquired with a Fourier lightfield microscope. The results obtained and presented in this paper are analyzed in terms of lateral resolution and of continuous and realistic parallax. We show that, in terms of these requirements, the proposed technique works efficiently in the case of the epi-illumination microscopy mode.

Testing Variational Bias Correction of Satellite Radiance Data in the ACCESS-C: Australian Convective-Scale NWP System.

Radiance observations are typically affected by biases that come mainly from instrument error (scanning or calibration) and inaccuracies of the radiative transfer model. These biases need to be removed for successful assimilation, so a bias correction scheme is crucial in the Numerical Weather Prediction (NWP) system. Today, most NWP centres, including the Bureau of Meteorology (hereafter, "the Bureau"), correct the biases through variational bias correction (VarBC) schemes, which were originally developed for global models. However, there are difficulties in estimating the biases in a limited-area model (LAM) domain. As a result, the Bureau's regional NWP system, ACCESS-C (Australian Community Climate and Earth System Simulator-City), uses variational bias coefficients obtained directly from its global NWP system ACCESS-G (Global). This study investigates independent radiance bias correction in the data assimilation system for ACCESS-C. We assessed the impact of using independent bias correction for the LAM compared with the operational bias coefficients derived in ACCESS-G between February and April 2020. The results from our experiment show no significant difference between the control and test, suggesting a neutral impact on the forecast. Our findings point out that the VarBC-LAM strategy should be further explored with different settings of predictors and adaptivity for a more extended period and over additional domains.

On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor.

This study described the on-orbit vicarious radiometric calibration of Chinese civilian high-resolution stereo mapping satellite ZY3-02 multispectral imager (MUX). The calibration was based on gray-scale permanent artificial targets, and multiple radiometric calibration tarpaulins (tarps) using a reflectance-based approach between July and September 2016 at Baotou calibration site in China was described. The calibration results reveal a good linear relationship between DN and TOA radiances of ZY3-02 MUX. The uncertainty of this radiometric calibration was 4.33%, indicating that radiometric coefficients of ZY3-02 MUX are reliable. A detailed discussion on the validation analysis of the comparison results between the different radiometric calibration coefficients is presented in this paper. To further validate the reliability of the three coefficients, the calibrated ZY3-02 MUX was compared with Landsat-8 Operational Land Imager (OLI). The results also indicate that radiometric characteristics of ZY3-02 MUX imagery are reliable and highly accurate for quantitative applications.

Derivation of maximum permissive switching time for automatic welding filter based on retinal thermal and blue light exposure limits.

Automatic welding filters are intended to protect the eyes of the welder from optical radiation generated by the welding process. Safety standards for automatic welding filters (AWFs) have been developed worldwide. However, switching time requirements have not been studied considering the exposure limits established by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) or the American Conference of Governmental Industrial Hygienists (ACGIH). The aim of this study is to derive the maximum permissive switching time based on ICNIRP guidelines for exposure to intense optical radiation. The single and cumulative exposures associated with retinal thermal and blue light hazards were assessed in the welding environment. The results show that the maximum switching time is inversely proportional to the exponential function of the shade number difference. The proposed maximum switching time, 1/2 to 1/10 smaller than the current ISO, is derived from the effective blue light radiance limits. In addition, an evaluation of the periodic short circuit welding indicates that AWFs do not transmit hazardous levels of radiation in the dark state.

Sky radiance and spectral gradient are orienting cues for the sandhopper Talitrus saltator (Crustacea, Amphipoda).

The sandhopper Talitrus saltator relies on both the sun and the moon compasses to return to the belt of damp sand on the beach in which it lives buried during the day. In addition to the sun, the gradient of radiance and the spectral distribution across the sky could provide directional information that T. saltator can potentially use to orient itself during the day even when the sun is not visible (e.g. cloudy sky). The scope of this work was (1) to determine the intensity levels of sky radiance that the sandhoppers use in their zonal recovery and (2) to investigate whether this species relies on the celestial spectral gradient in its zonal recovery. Sandhoppers were tested in the laboratory under artificial radiance or spectral gradients. Our results show that under an artificial sky simulating the natural radiance gradient on a cloudless day, sandhoppers orientated toward the correct seaward direction of their home beach; however, individuals lost their ability to use the intensity gradient as an orientation cue when the radiance was attenuated by at least 40%. Sandhoppers were also able to head in the correct seaward direction of their home beach at any time of the day by using the spectral gradient as their only source of visual orientation reference.

Modulations in irradiance directed at melanopsin, but not cone photoreceptors, reliably alter electrophysiological activity in the suprachiasmatic nucleus and circadian behaviour in mice.

Intrinsically photosensitive retinal ganglion cells convey intrinsic, melanopsin-based, photoreceptive signals alongside those produced by rods and cones to the suprachiasmatic nucleus (SCN) circadian clock. To date, experimental data suggest that melanopsin plays a more significant role in measuring ambient light intensity than cone photoreception. Such studies have overwhelmingly used diffuse light stimuli, whereas light intensity in the world around us varies across space and time. Here, we investigated the extent to which melanopsin or cone signals support circadian irradiance measurements in the presence of naturalistic spatiotemporal variations in light intensity. To address this, we first presented high- and low-contrast movies to anaesthetised mice whilst recording extracellular electrophysiological activity from the SCN. Using a mouse line with altered cone sensitivity (Opn1mwR mice) and multispectral light sources we then selectively varied irradiance of the movies for specific photoreceptor classes. We found that steps in melanopic irradiance largely account for the light induced-changes in SCN activity over a range of starting light intensities and in the presence of spatiotemporal modulation. By contrast, cone-directed changes in irradiance only influenced SCN activity when spatiotemporal contrast was low. Consistent with these findings, under housing conditions where we could independently adjust irradiance for melanopsin versus cones, the period lengthening effects of constant light on circadian rhythms in behaviour were reliably determined by melanopic irradiance, regardless of irradiance for cones. These data add to the growing evidence that modulating effective irradiance for melanopsin is an effective strategy for controlling the circadian impact of light.