Polarized imaging is widely used for separating specular reflection and diffuse reflection. By introducing the Bidirectional Reflectance Distribution Function (BRDF), the polarized characteristics of materials can be characterized. The polarimetric BRDF (pBRDF) describes the material's polarization properties through Mueller matrix-valued functions related to geometry, texture, and reflectance. Addressing the issue of inaccurate representation of surface reflection components for metal materials due to roughness and complex refractive index, this paper proposes a metal pBRDF modeling method. The method models the BRDF of the metal surface as a combination of single specular reflection and multiple inter-reflections. Experimental validation through Mueller matrix reconstruction demonstrates the accuracy and feasibility of this model.
The perspectives of light-field images taken by lens-based light-field camera are fixed due to the fixed position and size of lenslet. Unlike lens-based light-field imaging, lensless light-field imaging technologies encode light-field information of scene through a diffuser, and then reconstruct the light-field images by computational algorithms. In this paper, a commercial holographic diffuser is employed in the lensless light-field imaging system, and a flexible complementary segmentation method of sub-aperture region is proposed for the Point Spread Function (PSF). The surface of the holographic diffuser is composed of relief microstructures, which are pseudo-random and non-periodic. Hence, any region of the holographic diffuser can encode and recover light information of object independently, just like sub-aperture imaging from corresponding perspective. Firstly, the PSF of lensless light-field imaging system is calibrated, and the encoded image of scene is also captured. After that, the PSF is divided into two complementary regions as sub-PSFs, corresponding to different sub-aperture regions. Then sub-aperture images of scene are reconstructed with corresponding sub-PSFs through reconstruction algorithm, achieving light-field image of selected perspective. Properly dividing the sub-PSF and reconstructing sub-aperture image, the sub-aperture image array of light-field can be obtained. With these light-field images, digital refocusing can be achieved finally. Different from existing segmentation method of PSF in lensless light-field imaging, the complementary segmentation method divides PSF into two sub-PSFs while these tow sub-PSFs are complementary to each other. This allows us to arbitrarily select the position and size of sub-PSF flexibly, namely flexible perspective selection. Meanwhile, decreasing the sub-aperture images to be reconstructed in algorithm can reduce error in reconstruction. Experiments show that, the complementary segmentation method can freely select the position and size of sub-aperture region while reducing the reconstruction error of lensless light-field imaging system significantly.
2D raw image of Light Field (LF) camera needs to be decoded into 4D LF data for representation and processing. In decoding, the main lens is usually modeled as a thin lens while the micro-lens array is modeled as a pinhole array. Although this model takes into account the main lens distortion, it is still difficult to accurately characterize the complex imaging relationship of the LF camera. In order to obtain more accurate 4D LF data, this paper proposes a LF camera decoding method based on a two-plane ray model. By calibrating the ray corresponding to the pixel point of the LF camera, the mapping relationship between the real object point and the image point is established to obtain a new two- plane model of LF; then by interpolating and resampling on this two-plane, the correction of 4D LF data is achieved. Compared with traditional methods, our method improves the decoding accuracy of LF camera, and provides new ideas and methods for studying LF imaging.
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