基于收缩场学习的Retinex低照度图像增强

Learning shrinkage fields for low-light image enhancement via Retinex

为增强低照度图像和抑制噪声,提出了一种通过学习收缩场(SF)改进Retinex分解的图像增强方法。首先,构造新的目标函数,在正则项中引入2组不同的高阶滤波器分别约束未知的反射图和照明图。高阶滤波器可以学习到多种激活模式,有利于在恢复反射图的同时抑制噪声污染。然后,在优化目标函数时通过求解收缩场更新隐变量,参数化的压缩函数可以自适应地调整相应滤波器在反射图和照明图上的响应。最后,在每个级联内更新照明图之前,嵌入一个辅助的收缩场,以抑制噪声和不良伪影的传播,从而更精确地估计照明图。实验结果表明,所提方法取得的峰值信噪比(PSNR)和结构相似性(SSIM)均高于当前最新的低照度图像增强方法。

To enhance a low-light image and mitigate noises simultaneously,we propose an image enhancement algorithm by learning Shrinkage Field(SF)to improve the reflectance image and the illumination map in Retinex model.To this end,first,we design a novel objective function by constraining the latent reflectance image and the illumination map with two different groups of high-order filters in regularization terms.These filters can be learnt to possess various activation patterns and thus facilitate recovering the reflectance image and suppressing noises at the same time.Then,we update the latent variables in the objective function optimization by calculating SFs,where the parameterized shrinkage functions are capable to scale the responses of the corresponding high-order filters convolving the reflectance image and the illumination map.Finally,we embed an auxiliary SF model before the update of the illumination map in each cascade to suppress the propagation of noises and undesirable artifacts and further to refine the estimation of illumination map.Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art low-light image enhancement methods in terms of Peak Signal to Noise Ratio(PSNR)and Structural Similarity(SSIM).