Kavli Affiliate: Feng Wang
| First 5 Authors: Zhengyang Lu, Bingjie Lu, Feng Wang, ,
| Summary:
Physical and optical factors interacting with sensor characteristics create
complex image degradation patterns. Despite advances in deep learning-based
super-resolution, existing methods overlook the causal nature of degradation by
adopting simplistic black-box mappings. This paper formulates super-resolution
using structural causal models to reason about image degradation processes. We
establish a mathematical foundation that unifies principles from causal
inference, deriving necessary conditions for identifying latent degradation
mechanisms and corresponding propagation. We propose a novel counterfactual
learning strategy that leverages semantic guidance to reason about hypothetical
degradation scenarios, leading to theoretically-grounded representations that
capture invariant features across different degradation conditions. The
framework incorporates an adaptive intervention mechanism with provable bounds
on treatment effects, allowing precise manipulation of degradation factors
while maintaining semantic consistency. Through extensive empirical validation,
we demonstrate that our approach achieves significant improvements over
state-of-the-art methods, particularly in challenging scenarios with compound
degradations. On standard benchmarks, our method consistently outperforms
existing approaches by significant margins (0.86-1.21dB PSNR), while providing
interpretable insights into the restoration process. The theoretical framework
and empirical results demonstrate the fundamental importance of causal
reasoning in understanding image restoration systems.
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