Image Enhancement Based on Histogram-Guided Multiple Transformation Function Estimation

Although recent deep learning-based algorithms have achieved significant performance improvements in various image enhancement tasks, most approaches have been developed using image-to-image translation, which is challenging to interpret and analyze the enhancement processes. Several attempts have been made to use the image-to-transformation function approach for better interpretability; however, they often fail to generate complex color mappings, degrading image quality. In this work, we develop a novel transformation function-based algorithm that estimates multiple transformation functions with different properties by exploiting both the spatial and statistical characteristics of the input image to describe complex color mapping. First, we extract the image features that capture spatial information, considering their channel correlations. Next, we estimate multiple transformation functions utilizing a cross-attention block to capture the relevance between spatial and statistical information in the input image and its histogram, respectively. We then estimate the weight maps indicating the pixel-wise contribution of each transformation function by exploiting the spatial correlation between the input and transformed images obtained by each transformation function. Finally, we obtain an enhanced image by taking the weighted sum of the transformed images and the corresponding weight maps. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms on various image enhancement tasks.