Local Contrast Enhancement Based on Adaptive Multiscale Retinex Using Intensity Distribution of Input Image

Abstract

As the dynamic range of a digital camera is narrower than that of a real scene, the captured image requires a tone curve or contrast correction to reproduce the information in dark regions. Yet, when using a global correction method, such as histogrambased methods and gamma correction, an unintended contrast enhancement in bright regions can result. Thus, a multiscale retinex algorithm using Gaussian filters was already proposed to enhance the local contrast of a captured image using the ratio between the intensities of an arbitrary pixel in the captured image and its surrounding pixels. The intensity of the surrounding pixels is estimated using Gaussian filters and weights for each filter, and to obtain better results, these Gaussian filters and weights are adjusted in relation to the captured image. Nonetheless, this adjustment is currently a subjective process, as no method has yet been developed for optimizing the Gaussian filters and weights according to the captured image. Therefore, this article proposes local contrast enhancement based on an adaptive multiscale retinex using a Gaussian filter set adapted to the input image. First, the weight of the largest Gaussian filter is determined using the local contrast ratio from the intensity distribution of the input image. The other Gaussian filters and weights for each Gaussian filter in the multiscale retinex are then determined using a visual contrast measure and the maximum color difference of the color patches in the Macbeth color checker. The visual contrast measure is obtained based on the product of the local standard deviation and locally averaged luminance of the image. Meanwhile, to evaluate the halo artifacts generated in large uniform regions that abut to form a high contrast edge, the artifacts are evaluated based on the maximum color difference between each color of the pixels in a patch in the Macbeth color and the averaged color in CIELAB standard color space. When considering the color difference for halo artifacts, the parameters for the Gaussian filters and weights representing a higher visual contrast measure are determined using test images. In addition, to reduce the induced graying-out, the chroma of the resulting image is compensated by preserving the chroma ratio of the input image based on the maximum chroma values of the sRGB color gamut in the lightness-chroma plane. In experiments, the proposed method is shown to improve the local contrast and saturation in a natural way. INTRODUCTIONHuman vision is a complicated automatic self-adapting system that is capable of seeing over 5 orders of magnitude simultaneously and can gradually adapt to natural world scenes with a high dynamic range of over 9 orders of magnitude. Thus, human vision can concurrently perceive

Contrast Enhancement of Scanning Electron Microscopy Images Using a Noncomplex Multiphase Algorithm

“…The used metrics are visual contrast measure (VCM) (Jang et al, 2011), blind pseudoreference image (BPRI) (Min et al, 2017), and blind reference-less image spatial quality evaluator (BRISQUE) (Mittal, Moorthy & Bovik, 2012). The VCM is a metric that utilizes local statistical methods to measure the visual contrast in an image.…”

Hierarchical Integrated Color Matching in a Stereoscopic Image based on Image Decomposition

“…Thus, for local color matching, the local illuminants in the reference image and degraded image are estimated using the multi-scale retinex algorithm. [15][16][17][18] The estimated local illuminant in the degraded image is then compensated to match the estimated local illuminant in the reference image.…”

Quantitative Evaluation of Base and Detail Decomposition Filters Based on their Artifacts

“…Jang et al [35] proposed a method to automatically adjust the parameters of the multi-scale retinex algorithm, so as to optimize the perceptual quality of the contrast-enhanced image. A measure of the contrast was obtained using local standarddeviations.…”