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.