Lighting Estimation of a Convex Lambertian Object Using Redundant Spherical Harmonic Frames

An explicit lighting estimation from a single image of Lambertian objects is influenced by two factors： data incompletion and noise contamination. Measurement of lighting consistency purely using the orthogonal spherical harmonic basis cannot achieve an accurate estimation. We present a novel signal-processing framework to represent the lighting field. We construct a redundant spherical harmonic frame with geometric symmetry on the sphere S2. Spherical harmonic frames are defined over the generating rotation matrices about symmetry axes of finite symmetry subgroups of SO（3）, and the generating functions are spherical harmonic basis functions. Compared with the orthogonal spherical harmonic basis, the redundant spherical harmonic frames not only describe the multidirectional lighting distribution intuitively, but also resist the noise theoretically. Subsequently, we analyze the relationship of the irradiance to the incoming radiance in terms of spherical harmonic frames, and reconstruct the lighting function filtered by the Lambertian BRDF （bidirectional reflectance distribution function）. The experiments show that the frame coefficients of spherical harmonic frames can better characterize the complex lighting environments finely and robustly.

Underwater image enhancement by maximum-likelihood based adaptive color correction and robust scattering removal

Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean engineering.Although extensive research has been conducted into underwater image enhancement,little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes.In this paper,we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters,which effectively removes color casts of a variety of underwater images.A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed,which circumvents the influence of white or bright regions that challenges existing physical model-based methods.To enhance contrast of resultant images,a piece-wise affine transform is applied to the transmission map estimated via background light differential.Finally,with the estimated background light and transmission map,the scene radiance is recovered by addressing an inverse problem of image formation model.Extensive experiments reveal that our results are characterized by natural appearance and genuine color,and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics,which further validates the better robustness and higher generalization ability of our enhancement model.