A Novel Fusion Method for Low Brightness Enhancement Derivatives

In this paper, a straightforward and effective fusion method is designed for low brightness enhancement derivatives, which are generated through using brightness enhancement technique for a single low-brightness image. First, illumination estimation techniques and the principle of retinal imaging and cerebral cortex adjustment are combined to acquire the exposure ratio map. Then, a novel Chi-squared conversion function model and an accurate exposure ratio map are employed to obtain two derivatives with different characteristics: one is natural but not very detailed; the other is excessively bright but with prominent details. Finally, the improved weight matrix design and a novel derivatives fusion method are utilized to fuse the improved features of the derivatives. Experiments on a diverse set of images demonstrate that the proposed algorithm can not only reveal the efficiency of the brightness and detail enhancement, but also can show its superiority over several state-of-the-art processes in terms of overall visual information enhancement.

Automated summary

This paper introduces a fusion method for enhancing low brightness images, which combines various techniques to obtain natural and detailed image derivatives. By utilizing a novel model and weight matrix design, the proposed algorithm shows superior performance in enhancing overall visual information.