From Generation to Correction: Autoregressive Bitwise Correction for Image Super-Resolution

Visual autoregressive (VAR) models have achieved remarkable progress in image generation, which has boosted their application to image super-resolution (ISR). However, existing VAR-based ISR methods tend to follow a generative paradigm conditioned on the low-quality (LQ) representation directly, without explicitly disentangling reliable content from degradation-induced discrepancies. This work focuses on bitwise VAR models that operate by quantizing latent features into binary codes. Within this binary latent space, we systematically analyze the bitwise behavior between LQ and high-quality (HQ) representations. We observe that LQ and HQ representations are highly similar at the bit level, motivating us to revisit ISR through an LQ-anchored bitwise correction formulation. This formulation aims to identify and correct LQ bits that disagree with their HQ counterparts while preserving consistent ones. This selective correction enables faithful restoration by leveraging reliable LQ information and concentrating the generative prior on degradation-induced discrepancies for high-frequency detail synthesis. Building on this insight, we propose Generative-Prior-enhanced Autoregressive Bitwise Correction (GPABC), a framework that autoregressively predicts LQ-relative flip masks by leveraging corrected representations from preceding scales as context alongside a pretrained VAR prior. To balance reconstruction fidelity and perceptual quality, we further introduce a scale-adaptive asymmetric focal flip loss. This loss penalizes false flips at coarse scales to preserve reliable LQ content, while penalizing missed flips at fine scales to recover high-frequency details with the generative prior. Extensive quantitative and qualitative experiments across four benchmarks show that GPABC outperforms state-of-the-art methods in perceptual quality while maintaining competitive reconstruction fidelity, validating the effectiveness of the proposed bitwise correction formulation for ISR.