AVINet: adaptive variational iteration network for low light image enhancement - Tao Chen; Feng-Fei Jin; Jinqi Cui; Xiangyu Man; Dongmei Liu (Journal of King Saud University: Computer and Information Sciences)
Abstract Images captured in dark environments often face challenges like low contrast and noise. Many enhancement methods based on variational models, set parameters artificially to improve contrast, which often does not adequately address real-world conditions. To address this, we propose an adaptive variational model and establish an Adaptive Variational Iteration Network (AVINet). By designing a Parameter Extraction Module, we can adaptively learn the image contrast adjustment parameters within the model. Then, to prevent uneven image brightness, we introduce a smoothness term. We have mathematically proven the existence and uniqueness of the solution of this variational model, and then solve it using the Bregman iteration method. In the Denoising Module, we employ a denoising diffusion probability model to eliminate the noise in the image. Comparative experiments conducted on various datasets demonstrate that that our method achieves better results in restoring illumination and detail compared to other advanced methods.