Two methods are described to accurately estimate diffuse and specular reflectance parameters for colors, gloss
intensity and surface roughness, over the dynamic range of the camera used to capture input images. Neither
method needs to segment color areas on an image, or to reconstruct a high dynamic range (HDR) image. The
second method improves on the first, bypassing the requirement for specific separation of diffuse and specular
reflection components. For the latter method, diffuse and specular reflectance parameters are estimated separately,
using the least squares method. Reflection values are initially assumed to be diffuse-only reflection
components, and are subjected to the least squares method to estimate diffuse reflectance parameters. Specular
reflection components, obtained by subtracting the computed diffuse reflection components from reflection
values, are then subjected to a logarithmically transformed equation of the Torrance-Sparrow reflection model,
and specular reflectance parameters for gloss intensity and surface roughness are finally estimated using the least
squares method. Experiments were carried out using both methods, with simulation data at different saturation
levels, generated according to the Lambert and Torrance-Sparrow reflection models, and the second method,
with spectral images captured by an imaging spectrograph and a moving light source. Our results show that
the second method can estimate the diffuse and specular reflectance parameters for colors, gloss intensity and
surface roughness more accurately and faster than the first one, so that colors and gloss can be reproduced more
efficiently for HDR imaging.
Since commercial image detectors, such as charge-coupled device (CCD) cameras, have a limited dynamic range, it is difficult to obtain images that really are unsaturated, as a result of which the reflectance parameters may be inaccurately estimated. To solve this problem, we describe a method to estimate reflectance parameters from saturated spectral images. We separate reflection data into diffuse and specular components at 5-nm intervals between 380nm and 780nm for each pixel of the spectral images, which are captured at different incident angles, and estimate the diffuse reflectance parameters by applying the Lambertian model to the diffuse components. To estimate the specular reflectance parameters from the specular components, we transform the Torrance-Sparrow equation to a linear form, assuming Fresnel reflectance is constant. We then estimate specular parameters for intensity of the specular reflection and standard deviation of the Gaussian distribution, using the least squares method from unsaturated values of the specular components. Since Fresnel reflectance contributes to the physically based Torrance-Sparrow model in computer graphics and vision, we estimate both the Fresnel reflectance in terms of the Fresnel equation for the incident angle and the refractive index of the surface for dielectric materials, which varies with wavelength. We carried out experiments with measured data, and with simulated specular components at different saturation levels, generated according to the Torrance-Sparrow model. Our experimental results reveal that the diffuse and specular reflectance parameters are estimated with high quality.
We propose a new technique to reproduce faithfully both the color and the gloss of an object on a computer, using multispectral images. An imaging spectrograph equipped with a monochrome charge-coupled device (CCD) camera is fixed in front of the target object. Multispectral images of a linear portion of the object's surface are captured at suitable intervals by a measuring system which comprises a light source orbiting the target object. To obtain spectral images for the whole surface, the target object is also rotated. The reflection is separated into diffuse and specular components, according to the dichromatic reflection model, and the diffuse parameters are estimated at 5-nm intervals between 380nm and 780nm for each pixel. Since the CCD camera used to capture images has a limited dynamic range, we suppose that the specular reflection is independent of wavelength for the dielectrics, and that the specular reflections are saturated, although some of them can be non-saturated. We adopt the Torrance-Sparrow reflectance model for the specular reflection, and estimate the specular parameters using the least squares method for each pixel. Our experimental results reveal that the diffuse parameters for the color and the specular parameters for the gloss of the target object are satisfactorily estimated.