A multiscale retinex for bridging the gap between color images and the human observation of scenes

Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour.

[1]  A. Oppenheim,et al.  Nonlinear filtering of multiplied and convolved signals , 1968 .

[2]  R. Haber,et al.  Visual Perception , 2018, Encyclopedia of Database Systems.

[3]  O. Faugeras Digital color image processing within the framework of a human visual model , 1979 .

[4]  E H Land,et al.  Recent advances in retinex theory and some implications for cortical computations: color vision and the natural image. , 1983, Proceedings of the National Academy of Sciences of the United States of America.

[5]  E. Land Recent advances in retinex theory , 1986, Vision Research.

[6]  E H Land,et al.  An alternative technique for the computation of the designator in the retinex theory of color vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[7]  A Hurlbert,et al.  Formal connections between lightness algorithms. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[8]  P. Lennie,et al.  Mechanisms of color vision. , 1988, Critical reviews in neurobiology.

[9]  T. Poggio,et al.  Synthesizing a color algorithm from examples. , 1988, Science.

[10]  A. Hurlbert The Computation of Color , 1989 .

[11]  Geoffrey C. Fox,et al.  A VLSI Neural Network for Color Constancy , 1990, NIPS.

[12]  Rodney M. Goodman,et al.  A real-time neural system for color constancy , 1991, IEEE Trans. Neural Networks.

[13]  S. Pizer,et al.  The Image Processing Handbook , 1994 .

[14]  Rahman Zia Properties of a Center/Surround Retinex: Part 1. Signal Processing Design , 1995 .

[15]  A. W. Glenn,et al.  Properties of a Center/Surround Retinex: Part 2. Surround Design , 1995 .

[16]  Zia-ur Rahman,et al.  Multi-scale retinex for color image enhancement , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[17]  Rahman Zia-ur,et al.  A Multiscale Retinex for Color Rendition and Dynamic Range Compression , 1996 .

[18]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

[19]  W. Swanson Human Color Vision. 2nd ed. , 1998 .