SFP: Real-World Scene Recovery Using Spatial and Frequency Priors

TL;DR

This paper introduces a novel scene recovery method combining spatial and frequency priors to effectively restore real-world degraded images, outperforming existing approaches in diverse scenarios.

Contribution

The paper proposes a dual-prior framework leveraging spatial and frequency information for improved real-world scene recovery, addressing limitations of prior methods.

Findings

Outperforms existing methods in various degradation scenarios

Effectively estimates transmission maps from degraded images

Enhances frequency components adaptively for better restoration

Abstract

Scene recovery serves as a critical task for various computer vision applications. Existing methods typically rely on a single prior, which is inherently insufficient to handle multiple degradations, or employ complex network architectures trained on synthetic data, which suffer from poor generalization for diverse real-world scenarios. In this paper, we propose Spatial and Frequency Priors (SFP) for real-world scene recovery. In the spatial domain, we observe that the inverse of the degraded image exhibits a projection along its spectral direction that resembles the scene transmission. Leveraging this spatial prior, the transmission map is estimated to recover the scene from scattering degradation. In the frequency domain, a mask is constructed for adaptive frequency enhancement, with two parameters estimated using our proposed novel priors. Specifically, one prior assumes that the mean intensity of the degraded image's direct current (DC) components across three channels in the frequency domain closely approximates that of each channel in the clear image. The second prior is based on the observation that, for clear images, the magnitude of low radial frequencies below 0.001 constitutes approximately 1% of the total spectrum. Finally, we design a weighted fusion strategy to integrate spatial-domain restoration, frequency-domain enhancement, and salient features from the input image, yielding the final recovered result. Extensive evaluations demonstrate the effectiveness and superiority of our proposed SFP for scene recovery under various degradation conditions.