Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications

This paper presents the development of a new algorithm for Gaussian based color image enhancement system. The algorithm has been designed into architecture suitable for FPGA/ASIC implementation. The color image enhancement is achieved by first convolving an original image with a Gaussian kernel since Gaussian distribution is a point spread function which smoothen the image. Further, logarithm-domain processing and gain/offset corrections are employed in order to enhance and translate pixels into the display range of 0 to 255. The proposed algorithm not only provides better dynamic range compression and color rendition effect but also achieves color constancy in an image. The design exploits high degrees of pipelining and parallel processing to achieve real time performance. The design has been realized by RTL compliant Verilog coding and fits into a single FPGA with a gate count utilization of 321,804. The proposed method is implemented using Xilinx Virtex-II Pro XC2VP40-7FF1148 FPGA device and is capable of processing high resolution color motion pictures of sizes of up to 1600x1200 pixels at the real time video rate of 116 frames per second. This shows that the proposed design would work for not only still images but also for high resolution video sequences.Comment: 15 pages, 15 figure

Using retinex for point selection in 3D shape registration

Inspired by retinex theory, we propose a novel method for selecting key points from a depth map of a 3D freeform shape; we also use these key points as a basis for shape registration. To find key points, first, depths are transformed using the Hotelling method and normalized to reduce their dependence on a particular viewpoint. Adaptive smoothing is then applied using weights which decrease with spatial gradient and local inhomogeneity; this preserves local features such as edges and corners while ensuring smoothed depths are not reduced. Key points are those with locally maximal depths, faithfully capturing shape. We show how such key points can be used in an efficient registration process, using two state-of-the-art iterative closest point variants. A comparative study with leading alternatives, using real range images, shows that our approach provides informative, expressive, and repeatable points leading to the most accurate registration results. © 2014 Elsevier Ltd

Retinex-Based Low Contrast Image Enhancement Using Adaptive Tone-Mapping

Department of Electrical EngineeringIn this paper, we enhance low contrast images using the human visual system based Retinex theory and adaptive tone-mapping. We try to reduce halo artifact and color inconsistency, but also preserve naturalness of images. In the proposed algorithm, we process only the Y channel of the Yuv color space rather than RGB color space to maintain color-constancy. We first apply an adaptive bilateral filtering on the Y channel image to alleviate halo artifact during enhancement. Then we partition the intensity range of probability distribution of filtered Y channel image into low, middle, and high contrast regions according to a cost function. We improve the contrast of filtered Y channel image by using A-law based tone mapping by stretching the low contrast regions and compressing the high contrast regions adaptively. Experimental results show that the proposed algorithm enhances the visibility of input low contrast images efficiently.ope