Applications based on luminance processing often face the problem of recovering the original chrominance in the output color image. A common approach to reconstruct a color image from the luminance output is by preserving the original hue and saturation. However, this approach often produces a highly colorful image which is undesirable. We develop a color preservation method that not only retains the ratios of the input tri-chromatic values but also adjusts the output chroma in an appropriate way. Linearizing the output luminance is the key idea to realize this method. In addition, a lightness difference metric together with a colorfulness difference metric are proposed to evaluate the performance of the color preservation methods. It shows that the proposed method performs consistently better than the existing approaches.
Edge detection provides a representation that is used by all later stages of image processing. An edge representation
must be perceptually complete. This means that a perceptually accurate estimate of the original image can be
reconstructed from its edges. We present an algorithm for image reconstruction from edges using a redundant filter bank
derived by a wavelet frame composed of derivatives of Gaussian functions. Edges of an image can be extracted by any
edge detection method. The band-pass filtered image from this filter bank contains complete information of the original
image. A two-stage scheme of image reconstruction from edges is established. First, the band-pass filtered image is
approximated by a set of elementary functions specified by the initial edge parameters. The elementary functions
comprise of the first three orders of Gaussian derivatives. Second, the optimally approximated band-pass filtered image
plus the average of the image is applied to reconstruct the original image. We adopt a conjugate gradient algorithm for
the reconstruction. The major contribution of this paper is to lay down a theory of edge-based image reconstruction
A computational model for estimation of image motion from a sequence of images obtained by an imaging sensor is developed through mathematical modeling. The paper is divided into three parts which describe the evolution of the model of image motion. An ideal model of image motion is developed in the first part. The model is unsolvable because some parameters and boundary condition are unknown. Hence, a reformulated model of image motion is derived in the second part. Although this model is solvable, it is ill-posed mainly due to the differentiation of noise- contaminated image irradiance function. The consequence of this is that the solution estimated by this model is unstable. Thus, the reformulated model is remedied and transformed into a realistic model of image motion which is discussed in the third part. The results from simulations demonstrate that this realistic model of image motion gives correct and reliable estimation.