IRVR: A General Image Restoration Framework for Visual Recognition

Images corrupted with degradations often result in a performance drop in downstream image recognition models trained on clean images. Previous image restoration (IR) methods either restore the images without delicately considering the semantic recovery, or the training objectives cannot meet unseen recognition models, leading to poor and non-generalizable performance for various downstream recognition tasks. In this paper, we propose a general Image Restoration framework for Visual Recognition (IRVR), which addresses generalized and effective semantic recovery in image restoration for a range of high-level tasks. Concretely, for better generalization, we train the IR models with semantic recovery as the primary objective, and image regression as a regularization term, respectively, where the primary objective gradient is calibrated with the regularization gradient to ensure the generalization of IR to unseen recognition models. For effectiveness, we introduce an intrinsic semantic consistency constraint to match the semantic statistical distribution between restored and clean image pairs. Our IRVR is recognition-agnostic and orthogonal to IR, making it a plug-and-play component that can be incorporated into existing IR methods without adding any computational cost during inference. Extensive experiments demonstrate the effectiveness and generalization of our IRVR for improving the performance of IR in diverse downstream high-level tasks. The IRVR's ability to accurately recover intrinsic semantics in images is instrumental in high-level machine analysis, which ensures the integrity and authenticity of multimedia content.