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 smoothes 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 1600×1200 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.
The limitations mentioned earlier are overcome in the proposed method in an efficient way. To start with, the input image is convolved with @math Gaussian kernel in order to smooth the image. Further, the dynamic range of an image is compressed by replacing each pixel with its logarithm. In the proposed method, the image enhancement operations are arranged in an efficient way adding true color constancy at every step. It has less number of parameters to specify and provides true color fidelity. In addition, the proposed algorithm is computationally inexpensive. In the proposed scheme, an additional step is necessary to solve the gray world violation problem as is the case with the implementation reported in Ref. @cite_1 .
@cite_1
2464766066 15713842 1994796550 2536722097
The aim of this paper is twofold. First, we propose a new method for enhancing the contrast of gray-value images. We use the difference of the average local contrast measures between the original and the enhanced images within a variational framework. This enables the user to intuitively control the contrast level and the scale of the enhanced details. Moreover, our model avoids large modifications of the original image histogram. Thereby it preserves the global illumination of the scene and it can cope with large areas having similar gray values. The minimizer of the proposed functional is computed by a gradient descent algorithm in connection with a polynomial approximation of the average local contrast measure. The polynomial approximation is computed via Bernstein polynomials. In the second part, the approach is extended to a variational enhancement method for color images. The model approximately preserves the hue of the original image and additionally includes a total variation term to correct the possible noise. The method requires no post- or preprocessing. The minimization problem is solved with a hybrid primal---dual algorithm. Experiments demonstrate the efficiency and the flexibility of the proposed approaches in comparison with state-of-the-art methods. In this paper, we propose to conduct inpainting and upsampling for defective depth maps when aligned color images are given. These tasks are referred to as the guided depth enhancement problem. We formulate the problem based on the heat diffusion framework. The pixels with known depth values are treated as the heat sources and the depth enhancement is performed via diffusing the depth from these sources to unknown regions. The diffusion conductivity is designed in terms of the guidance color image so that a linear anisotropic diffusion problem is formed. We further cast the steady state problem of this diffusion into the famous random walk model, by which the enhancement is achieved efficiently by solving a sparse linear system. The proposed algorithm is quantitatively evaluated on the Middlebury stereo dataset and is applied to inpaint Kinect data and upsample Lidar's range data. Comparisons to the commonly used bilateral filter and Markov Random Field based methods are also presented, showing that our algorithm is competent. Color image enhancement is a complex and challenging task in digital imaging with abundant applications. Preserving the hue of the input image is crucial in a wide range of situations. We propose simple image enhancement algorithms, which conserve the hue and preserve the range (gamut) of the R, G, B channels in an optimal way. In our setup, the intensity input image is transformed into a target intensity image whose histogram matches a specified, well-behaved histogram. We derive a new color assignment methodology where the resulting enhanced image fits the target intensity image. We analyze the obtained algorithms in terms of chromaticity improvement and compare them with the unique and quite popular histogram-based hue and range preserving algorithm of Naik and Murthy. Numerical tests confirm our theoretical results and show that our algorithms perform much better than the Naik-Murthy algorithm. In spite of their simplicity, they compete with well-established alternative methods for images where hue-preservation is desired. One source of difficulties when processing outdoor images is the presence of haze, fog or smoke which fades the colors and reduces the contrast of the observed objects. We introduce a novel algorithm and variants for visibility restoration from a single image. The main advantage of the proposed algorithm compared with other is its speed: its complexity is a linear function of the number of image pixels only. This speed allows visibility restoration to be applied for the first time within real-time processing applications such as sign, lane-marking and obstacle detection from an in-vehicle camera. Another advantage is the possibility to handle both color images or gray level images since the ambiguity between the presence of fog and the objects with low color saturation is solved by assuming only small objects can have colors with low saturation. The algorithm is controlled only by a few parameters and consists in: atmospheric veil inference, image restoration and smoothing, tone mapping. A comparative study and quantitative evaluation is proposed with a few other state of the art algorithms which demonstrates that similar or better quality results are obtained. Finally, an application is presented to lane-marking extraction in gray level images, illustrating the interest of the approach.