Bio-inspired color image enhancement

Abstract

Capturing and rendering an image that fulfills the observer's expectations is a difficult task. This is due to the fact that the signal reaching the eye is processed by a complex mechanism before forming a percept, whereas a capturing device only retains the physical value of light intensities. It is especially difficult to render complex scenes with highly varying luminances. For example, a picture taken inside a room where objects are visible through the windows will not be rendered correctly by a global technique. Either details in the dim room will be hidden in shadow or the objects viewed through the window will be too bright. The image has to be treated locally to resemble more closely to what the observer remembers. The purpose of this work is to develop a technique for rendering images based on human local adaptation. We take inspiration from a model of color vision called Retinex. This model determines the perceived color given spatial relationships of the captured signals. Retinex has been used as a computational model for image rendering. In this article, we propose a new solution inspired by Retinex that is based on a single filter applied to the luminance channel. All parameters are image-dependent so that the process requires no parameter tuning. That makes the method more flexible than other existing ones. The presented results show that our method suitably enhances high dynamic range images.

Image enhancement by unsharp masking the depth buffer

“…Furthermore, color vision models such as the Retinex model that is also applied for tone mapping, were used for locally amplifying contrasts, see e.g. [Meylan and Süsstrunk 2004]. Nevertheless, the missing depth information limits the possible effects.…”

Center/Surround Retinex: Analysis and Implementation

“…As proved in [14], the only radial kernel which is scale invariant is F (r) = K r 2 , which is the (nonintegrable) kernel proposed by Land in [7]. Several other radial kernels have been proposed in the literature [7,13,5,4,3,12,14,17]. In [10] an exhaustive analysis of these kernels is performed, and some new ones are proposed.…”

An investigation of Retinex algorithms for image enhancement

“…The first step taken by most Retinex algorithms is the conversion of the given image into the logarithmic domain by log , s S = log , l L = log r R = and thereby [3] . s l r = +…”