Optimized histogram computation model using cuckoo search for color image contrast distortion

This paper proposes a novel optimized histogram modification framework using a cuckoo search (CS) algorithm for brightness preserved contrast enhancement. The histogram modification has been modeled as an optimization problem and the CS algorithm is used to find an optimum solution. The proposed method's chief objective is to find a fair tradeoff between contrast improvement and mean-brightness preservation. A new target histogram has been generated using the CS algorithm using a target function composed of tuning parameters. The extent of the image contrast range can be adjusted by selecting appropriately designed tuning parameters. The proposed technique can control the enhancement with the values chosen to fix the enhancement level and retain the enhanced image features. Finally, the optimized histogram generated from the CS algorithm is employed in the histogram equalization (HE) process to generate the final enhanced image. Its performance is validated with qualitative and quantitative assessment against well-known state-of-the-art techniques. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a novel optimized histogram modification framework using a cuckoo search algorithm for brightness preserved contrast enhancement. The method aims to find a fair tradeoff between contrast improvement and mean-brightness preservation by generating a new target histogram using the CS algorithm with tuning parameters. The technique can control enhancement levels and retain enhanced image features by selecting appropriate values, and the performance is validated against state-of-the-art techniques.