Abstract
This article addresses the problem of low dynamic range image enhancement for commercial digital cameras. A novel simultaneous dynamic range compression and local contrast enhancement algorithm (SDRCLCE) is presented to resolve this problem in a single-stage procedure. The proposed SDRCLCE algorithm is able to combine with many existent intensity transfer functions, which greatly increases the applicability of the proposed method. An adaptive intensity transfer function is also proposed to combine with SDRCLCE algorithm that provides the capability to adjustably control the level of overall lightness and contrast achieved at the enhanced output. Moreover, the proposed method is amenable to parallel processing implementation that allows us to improve the processing speed of SDRCLCE algorithm. Experimental results show that the performance of the proposed method outperforms three state-of-the-art methods in terms of dynamic range compression and local contrast enhancement.
Highlights
In recent years, digital video cameras have been employed for video recording, and in a variety of image-based technical applications such as visual tracking, visual surveillance, and visual servoing
Experimental results we focus on four issues, which include a detailed examination of the properties of the proposed method, the quantitative comparison with three state-ofthe-art enhancement approaches, the visual comparison with the results produced by these methods, and computational speed evaluation
One merit of the proposed method is that the proposed SDRCLCE algorithm can combine with any monotonically increasing and continuously differentiable intensity transfer function, such as the typical gamma curve, to achieve dynamic range compression with local contrast preservation/enhancement for low dynamic range (LDR) images
Summary
Digital video cameras have been employed for video recording, and in a variety of image-based technical applications such as visual tracking, visual surveillance, and visual servoing. (4) By combining the proposed adaptive intensity transfer function with SDRCLCE algorithm, the proposed method possesses the adjustability to separately control the level of dynamic range compression and local contrast enhancement. This advantage improves flexibility of the proposed method in practical applications. If RGB coordinates are required, a simplified multiplicative model based on the chromatic information of the original image can be applied to recover the enhanced color image with minimum color distortion It PiRnGB = Rin Gin Bin T and PoRuGtB = Rout Gout Bout T denote the input and output color values of each pixel in RGB color space, respectively, the multiplicative model of linear color remapping in RGB color space is expressed as: PoRuGtB(x, y) = β(x, y) × PiRnGB(x, y),. Substituting (17) and (19) into (18), the proposed SDRCLCE method is able to preserve hue and saturation of the original image in the enhanced image
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