Blur-Kernel Estimation from Spectral Irregularities

In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images as well as some blurring functions, e.g., motion blur and out-of-focus blur. An alternating minimization (AM)implicit iterative scheme is devised to recover the image and simultaneously identify the point spread function (psf). Numerical results indicate that the iterative scheme is quite robust, converges very fast (especially for discontinuous blur), and both the image and the psf can be recovered under the presence of high noise level. Finally, we remark that psf's without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach.

Digitized records of terrain scenes were produced using a technique of photographic colorimetry. Each record consisted of three tristimulus images (X, Y, and Z) which were analyzed for their color statistics, spatial frequency content, and image correlation. Interactions between color and space were examined using a cone receptor transformation. It is shown that the scene amplitude spectra follow an approximate reciprocal variation with frequency, and that the correlation function can be described by a one-step autoregressive model. The results are discussed in terms of methods for optimum image coding in human and machine vision.