A GPU-Accelerated Adaptive Simultaneous Dynamic Range Compression and Local Contrast Enhancement Algorithm for Real-Time Color Image Enhancement

Abstract

Modern robotic vision systems usually require the use of image enhancement algorithms as they play an important preliminary processing role in computer vision applications. Numerous image enhancement methods have been proposed to deal with monochromatic images. However, they often cannot be directly applied on multichannel images, which usually give more information about the environment than monochromatic images. Consequently, color image enhancement has now become an active research issue in image enhancement processing. Moreover, because color images contain much more data to be processed, the improvement on computational efficiency of color image enhancement to achieve real-time performance is still a challenging task.This chapter presents a parallel implementation of an existing adaptive color image enhancement scheme, which improves visual quality of color images suffered from low dynamic range (LDR) and poor contrast defects. Images captured from a digital camera may be in poor visibility due to the limitation on dynamic range of image sensors. This problem typically can be resolved by compressing the dynamic range of captured images, and this image enhancement process is commonly known as dynamic range compression. Several dynamic range compression techniques have been proposed based on Retinex theory, which is a lightness and color perception model of human vision [1]. Retinex-based techniques [2][3][4][5][6][7] are general purpose methods, simultaneously achieving dynamic range compression, local-contrast enhancement, and color consistency; however, these algorithms are usually computationally expensive, requiring algorithmic simplification and hardware acceleration to achieve real-time performance [8].