Low Illumination Image Processing Based on Adaptive Threshold and Local Tone Mapping

In order to solve the problem of lack of detailed information and low definition of low illuminance images, the fusion algorithm of non-undersampled shear wave transform (NSST) and Retinex theory is used to process low illuminance images in the color space of HSV (Hue, Saturation, Value). First, the V component of the HSV space is decomposed to obtain multiple high pass subbands and a low pass subband. The high pass subbands with the improved adaptive threshold algorithm based on Bayesian shrinkage denoising, the low pass subbands with the improved adaptive local color mapping algorithm improve the contrast. Then, the NSST inverse transformation is applied to the two subbands to obtain the new V components and white balance treatment is performed on them. Finally, the processed image is reversed to the RGB (Red, Green, Blue) space to get the result image. Experimental results show that the proposed algorithm can improve the quality of low illuminance images, and improve the definition and contrast.