Digital Image Processing
Color image enhancement by fuzzy intensification
Pattern Recognition Letters
Properties of a Center/Surround Retinex: Part 2. Surround Design
Properties of a Center/Surround Retinex: Part 2. Surround Design
Associative memory using nonlinear line attractor network for multi-valued pattern association
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Image quality evaluation based on recognition times for fast image browsing applications
IEEE Transactions on Multimedia
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Hue-preserving color image enhancement without gamut problem
IEEE Transactions on Image Processing
A new design method for the complex-valued multistate Hopfield associative memory
IEEE Transactions on Neural Networks
On Non-Uniform Rational B-Splines Surface Neural Networks
Neural Processing Letters
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A novel method to map high dynamic range scenes to low dynamic range images utilizing the concept of color characterization, enhancement, and balancing is described in this letter. Each pixel of the image is first characterized by extracting the relationship of the red, green, and blue components along with its corresponding neighbors using a nonlinear line attractor network to form an associative memory. Then, the illumination enhancement process is performed using a hyperbolic tangent function to provide dynamic range compression to each pixel in the image. The slope of the hyperbolic tangent function is controlled using a parameter that is determined by the local and global statistics of the image to facilitate the change of the intensity level. A color balancing process restores the original color characteristics of the image based on learned associative memory matrices which eliminate image distortion due to improper recombination of red, green and blue components after enhancement. Experiments conducted on images captured at extremely uneven lighting environments show that the proposed method outperforms other image enhancement algorithms.