DichotomyIR: Universal Image Reconstruction via Dichotomy Classification and Uncertainty Elimination

摘要

L1 loss is a classical regression loss function and achieves remarkable success in image reconstruction (IR) tasks. Theoretically, the L1 loss is the maximum likelihood of images with the Laplace prior distribution, which is simple and not that suitable for digital images with integer pixel values. This phenomenon results in high reconstruction error in detailed areas (such as boundary areas). For this, we investigate the effectiveness of classification loss functions like Cross Entropy (CE) loss in IR tasks, which is the maximum likelihood of Multinomial (or Bernoulli) prior distribution, and propose a novel image reconstruction framework named DichotomyIR. To apply CE loss to image reconstruction, we adopt the dichotomy method to transfer integer pixel values into 8-bit labels and further design a dual-branch Dichotomy Decoder (D-Decoder) in DichotomyIR to reconstruct high-quality (HQ) images I_L1 and I_CE supervised with L1 loss and CE loss, respectively. Next, we analyze the reconstructed uncertainties of these 2 images with different prior distributions and design an iterative uncertainty elimination (IUE) processing. Integrating the popular status spatial model with the IUE processing, we propose the Uncertainty Elimination Mamba (UEM) to eliminate the reconstructed uncertainty iteratively. With the D-Decoder and UEM, the DichotomyIR is flexible and feasible to be embedded with any current popular IR methods. Universal experiments on IR tasks demonstrate the effectiveness and efficiency of the proposed DichotomyIR, which strongly supports the importance of uncertainty elimination in IR tasks.