Multi-scale Retinex With Color Restoration Research Articles

CONTRAST ENHANCEMENT OF COLOR IMAGES USING IMPROVED RETINEX METHOD

Color images provide large information for human visual perception compared to grayscale images. Color image enhancement methods enhance the visual data to increase the clarity of the color image. It increases human perception of information. Different color image contrast enhancement methods are used to increase the contrast of the color images. The Retinex algorithms enhance the color images similar to the scene perceived by the human eye. Multiscale retinex with color restoration (MSRCR) is a type of retinex algorithm. The MSRCR algorithm results in graying out and halo artifacts at the edges of the images. So here the focus is on improving the MSRCR algorithm by combining it with contrast limited adaptive histogram equalization (CLAHE) using image.

Novel multi-scale retinex with color restoration on graphics processing unit

The multi-scale retinex with color restoration (MSRCR) was developed as a general-purpose image enhancement algorithm that provides simultaneous dynamic range compression, local lightness/contrast enhancement, and good color rendition, and has been successfully used for a wide variety of imagery from diverse fields. While the MSRCR performs good enhancement in most images, the output image can sometimes be further visually optimized during our experiments. An improved MSRCR+Autolevels (AL) algorithm is presented, which can eliminate the impact of a small number of outliers in the histogram of the image and further improve the contrast of an image. New extension significantly improves the visual performance of the MSRCR algorithm. However, the MSRCR+AL containing a large number of complex calculations is computationally expensive, limiting real-time applications. In this paper, a parallel application of the MSRCR+AL algorithm on a graphics processing unit (GPU) is presented. For the various configurations in our test, the GPU-accelerated MSRCR+AL shows a scalable speedup as the resolution of an image increases. The up to 45× speedup (1,024 × 1,024) over the single-threaded CPU counterpart shows a promising direction of using the GPU-based MSRCR+AL in large-scale, time-critical applications. We also achieved 17 frames per second in video processing (1,280 × 720).

Color Image Enhancement Methods Based on Matlab

The aim of the paper is to provide three enhancement methods for processing color images, including: RGB Histogram equalization, the method based on HSI and Multi-scale Retinex with color restoration (MSRCR). Firstly, we shall briefly introduce color image enhancement and related concept. Then, emphasis is placed on the principle of the three methods. Afterwards, we make comparison among the three methods. Finally, we can get a conclusion that: MSRCR greatly not only strengthens the details but also keep the color of the original image and acts more consistent with characteristics of human vision. However, it takes longer time. Simulation results provide a guidance and reference for the choice of color image enhancement methods.

A Modified Multi Scale Retinex with Color Restoration Algorithm for Automatic Enhancement of Landsat-5 Remote Sensing Image

The retinex is a human perception based image processing algorithm which provides color constancy and dynamic range compression. The multi scale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. But the MSRCR results suffer from lower global brightness and partial color distortion. In order to improve the MSRCR method, this paper presents a modified MSRCR algorithm to Landsat-5 image enhancement considering percent liner stretch and histogram adjustment. Finally, the effect of modified MSRCR method on Landsat-5 image enhancement is analyzed and the comparison with other color adjustment methods such as gamma correction and histogram equalization is reported in the experimental results.

Retinex processing for automatic image enhancement

The <i>Journal of Electronic Imaging</i> (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.