New method of color image histogram equalization with gray replacement for underwater and medical images

Artyom M. Grigoryan; Irene Salazar; Sos S. Agaian

doi:10.1117/12.2663262

15 June 2023 New method of color image histogram equalization with gray replacement for underwater and medical images

Artyom M. Grigoryan, Irene Salazar, Sos S. Agaian

Proceedings Volume 12526, Multimodal Image Exploitation and Learning 2023 ; 125260U (2023) https://doi.org/10.1117/12.2663262
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

Deep learning-based image enhancement is challenging in underwater and medical imaging domains, where high-quality training data is often limited. Due to water distortion, loss of color, and contrast, images captured in these settings could be of better quality, making it easier to train deep learning models effectively on large and diverse datasets. This limitation can negatively impact the performance of these models. This paper proposes an alternative approach to supervised color image enhancement to address this challenge. Specifically, the authors propose to enhance images in both the spatial and frequency domains using their two × two model quaternion image structure, which was previously proposed. The color image components plus gray or brightness are map into the grayscale image of twice size and then HE of new grays is calculated. The new colors and gray of the image are reconstructed from the new image in two × two model. The approach is tested extensively through computer simulations, demonstrating that the proposed framework achieves competitive performance in quantitative and qualitative metrics compared to state-of-the-art approaches.

Citation Download Citation

Artyom M. Grigoryan, Irene Salazar, and Sos S. Agaian "New method of color image histogram equalization with gray replacement for underwater and medical images", Proc. SPIE 12526, Multimodal Image Exploitation and Learning 2023 , 125260U (15 June 2023); https://doi.org/10.1117/12.2663262

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