Improving uneven exposure using color characteristics as a priori information in endoscopic images

Endoscopic examinations often encounter color discrepancies in images due to uneven lighting and variations in color temperature of the light source, which directly affect the accuracy of disease screening and diagnosis. Traditional methods for enhancing low-quality endoscopic images primarily concentrate on correcting underexposed images, but do not simultaneously address properly exposed regions or suppress overexposed regions in the image. To address these issues, this study proposes a novel network structure, EndoUEI, which leverages color priors to improve over / underexposure of endoscopic images. The EndoUEI integrates an encoder-decoder framework with embedded color features, a Retinex exposure correction module, and an exposure fusion module. Within the encoder-decoder, an efficient multiscale attention module is employed to enhance feature representation and capture both short- and long-range pixel dependencies. Multi-scale color information are employed as color features to guide the segmenting of unevenly exposed areas. This approach effectively mitigates uneven exposure levels in low-quality endoscopic images while enriching the overall color information. On the colonoscopic dataset, the Peak Signal-to-Noise Ratio reached 21.06, while on the real nasopharyngeal dataset, the Natural Image Quality Evaluator was only 7.35, and the parameter number of the model is only 0.646 M. These results demonstrate that the EndoUEI significantly enhances the image quality of colonoscopy and nasopharyngoscopy while maintaining a minimal parameter count, thereby holding greater clinical applications.