contrast enhancement

Hybridization of Divide-and-Conquer technique and Neural Network algorithm for better contrast enhancement in medical images

The aim of this work is to propose a new method for optimal contrast enhancement of a medical image.  The main idea is to improve the Divide-and-Conquer method to enhance the contrast, and highlight the information and details of the image, based on a new conception of the Neural Network algorithm.  The Divide-and-Conquer technique is a suitable method for contrast enhancement with an efficiency that directly depends on the choice of weights in the decomposition subspaces.  A new hybrid algorithm was used for the optimal selection of weights, considering the optimizatio

A new mathematical model for contrast enhancement in digital images

The aim of this work is to propose a new mathematical model for optimal contrast enhancement of a digital image.  The main idea is to combine the Divide-and-Conquer strategy, and a reaction diffusion mathematical model to enhance the contrast, and highlight the information and details of the image, based on a new conception of the Sine-Cosine optimization algorithm.  The Divide-and-Conquer technique is a suitable method for contrast enhancement with an efficiency that directly depends on the choice of weights in the decomposition subspaces.     

A new Lattice Boltzmann method for a Gray–Scott based model applied to image restoration and contrast enhancement

The aim of this work is to propose a new numerical approach to image restoration and contrast enhancement based on a reaction-diffusion model (Gray–Scott model).  For noise removal, a Lattice Boltzmann technique is used.  This method is usually used in fluid dynamics experiments.  Since pixels motion can be compared to fluids motion, the presented technique also indicates a good performance in processing noisy images.  The efficiency and performance of the proposed algorithm are verified by several numerical experiments.