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Backlit image enhancement is a challenging research problem in image processing and computer vision. The complexity of enhancing backlit images is that they simultaneously contain bright and dark intensity regions. Existing state-of-the-art methods usually have low contrast enhancement in the dark regions, are prone to color distortion in the background or noise amplification, and have high computational complexity. Inspired by its potentiality, this paper applies region segmentation and a transfer learning-based approach to enhance the contrast of the dark regions in backlit images using multi-illumination mappings. In the proposed framework, gamma and log transform functions are applied to achieve a varied set of illumination-map to improve the brightness and contrast of the backlit image. The average results of the validation metrics of the proposed method for contrast measure (CM), entropy (E), CM/E ratio, and SSIM are 8.10, 7.52, 1.07, and 0.58, respectively. The experimental results corroborate that the proposed method demonstrates superior performance to state-of-the-art methods both subjectively and objectively. [ABSTRACT FROM AUTHOR]

Backlit images simultaneously include dark as well as light regions of intensity. Objects in dark and low-contrast regions are complex for humans to perceive. In particular, such parts are selected as the image's region of interest (ROI). In backlit images, ROI is typically very dark and of low contrast, which makes visualization difficult. This paper explores and presents an approach for contrast enhancement and preserving the naturalness of the ROI of the backlit image. The proposed methodology considers stretching the contrast and balancing other aspects of the ROI, such as textural details, visibility, etc. In the last stage, a fusion-based approach is carried out to obtain enhanced ROI of the backlit image. It also examines the experimental results of the proposed method using various steps and, based on the validation, confirms that the proposed solution produces better results for the ROI of the backlit image than the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Propose a novel method for contrast enhancement of ROI of backlit image for surveillance systems. • Gamma and Log tone maps improve the contrast and luminosity of the dark regions of the backlit image. • A transfer learning-based approach generates multi-illumination maps for the ROI of backlit images. • An average-based fusion method for multi-illumination maps amalgamation. The surveillance images under backlit conditions is a potential and challenging research problem in image processing and computer vision. Low contrast regions of interest of backlit images form an indiscernible part for the real-time surveillance and biometrics applications. However, current state-of-the-art techniques typically offer limited dark-region contrast enhancement, are susceptible to colour distortion. This research work uses region of interest segmentation and a transfer learning-based strategy to improve the contrast of the dark intensity regions in backlit image using multi-illumination mappings, motivated by its usefulness. To generate a diversified collection of illumination-maps and enhance the brightness and contrast of the backlit image, the proposed framework applies the varied set of log and gamma transforms. The experimental findings confirm the assertion that the proposed method provides better performance than state-of-the-art methods both objectively and subjectively. [ABSTRACT FROM AUTHOR]