A Comparison of the Multiscale Retinex With Other Image Enhancement Techniques

The multiscale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. A number of algorithms exist that provide one or more of these features, but not all. In this paper we compare the performance of the MSRCR with techniques that are widely used for image enhancement. Specifically, we compare the MSRCR with color adjustment methods such as gamma correction and gain/offset application, histogram modification techniques such as histogram equalization and manual histogram adjustment, and other more powerful techniques such as homomorphic filtering and ‘burning and dodging’. The comparison is carried out by testing the suite of image enhancement methods on a set of diverse images. We find that though some of these techniques work well for some of these images, only the MSRCR performs universally well on the test set. Introduction The Multiscale Retinex1 (MSR) is a generalization of the single-scale retinex2 4 (SSR), which, in turn, is based upon the last version of Land’s center/surround retinex5. The current version of the MSR combines the retinex dynamic range compression and color constancy with a color ‘restoration’ filter that provides excellent color rendition6 8. This version of the MSR is called the Multiscale Retinex with Color Restoration (MSRCR). The MSRCR has been tested with a very large suite of images and has consistently proven to be better than any conventional image enhancement technique. In this paper we present a comparison of the MSRCR with several of the most popular image enhancement methods. These include point transforms such as automatic gain/offset, non-linear gamma correction, non-linear intensity transforms such as the logarithmic transform or the ‘square-root’ transform; and global transforms such as histogram equalization9, homomorphic filtering10, and manual ‘burning and dodging.’ State-of-the-art Techniques In this section we briefly describe the characteristics of some of the state-of-the-art techniques most commonly used for image enhancement. Gain/offset correction One of the most common methods of enhancing an image is the application of a gain and an offset to stretch the dynamic range of an image. This is a linear operation and hence has limited success on scenes that encompass a much wider dynamic range than that that can be displayed. In this case, loss of detail occurs due to saturation and clipping as well as due to poor visibility in the darker regions of the image. For a scene with dynamic range between and , and a display medium with dynamic range , this transform can be represented by