Low-Light Image Enhancement Based on Nonsubsampled Shearlet Transform

Abstract

To improve the observability of low-light images, a low-light image enhancement algorithm based on nonsubsampled shearlet transform (NSST) is presented (LIEST). The proposed algorithm can synchronously achieve contrast improvement, noise suppression, and the enhancement of specific directional details. An enhancement framework of low-light noisy images is first derived, and then, according to the framework, a low-light noisy image is decomposed into low-pass subband coefficients and bandpass direction subband coefficients by NSST. Then, in the NSST domain, an illumination map is estimated based on a bright channel of the low-pass subband coefficients, and noise is simultaneously suppressed by shrinking the bandpass direction subband coefficients. Finally, based on the estimated illumination map, the low-pass subband coefficients, and the shrunken bandpass direction subband coefficients, inverse NSST is implemented to achieve low-light image enhancement. Experiments demonstrate that the LIEST exhibits superior performance in improving contrast, suppressing noise, and highlighting specific details as compared to seven similar algorithms.