Scale-progressive Multi-patch Network for image dehazing

Abstract

Image dehazing is a classical vision task, which aims to recover a clean image from a hazy one. Previous dehazing methods usually follow a coarse-to-fine architecture to mine clean features by introducing generic CNNs components. However, this manner usually results in undesirable model complexity and computational burden. In this work, we present a Scale-progressive Multi-patch Network (SPM-Net) to handle common problems in previous dehazing networks. Specifically, our approach utilizes a scale-progressive multi-patch mechanism to efficiently model uneven hazy distribution on local patches and progressively explore clean cues in multiple scales in a fine-to-coarse way. Besides of above, we found the feature misalignment problem in the patch-based methods, and a practical solution is proposed to handle this previously neglected problem. A comprehensive evaluation of both synthetic datasets and real-world datasets demonstrates that the proposed dehazing method surpasses the previous state-of-the-art approaches with a margin both quantitatively and qualitatively. Our proposed SPM-Net achieved a PSNR of 29.47 dB on the Haze4k dataset, significantly surpassing the previous state-of-the-art method DMT-Net (29.47 dB vs. 28.53 dB) while having much fewer parameters than DMT-Net (16.1 M vs. 54.9 M) and faster inferencing efficiency (0.072 s vs. 0.192 s).