Online knowledge distillation network for single image dehazing

Abstract

Single image dehazing, as a key prerequisite of high-level computer vision tasks, catches more and more attentions. Traditional model-based methods recover haze-free images via atmospheric scattering model, which achieve favorable dehazing effect but endure artifacts, halos, and color distortion. By contrast, recent learning-based methods dehaze images by a model-free way, which achieve better color fidelity but tend to acquire under-dehazed results due to lacking of knowledge guiding. To combine these merits, we propose a novel online knowledge distillation network for single image dehazing named OKDNet. Specifically, the proposed OKDNet firstly preprocesses hazy images and acquires abundant shared features by a multiscale network constructed with attention guided residual dense blocks. After that, these features are sent to different branches to generate two preliminary dehazed images via supervision training: one branch acquires dehazed images via the atmospheric scattering model; another branch directly establishes the mapping relationship between hazy images and clear images, which dehazes images by a model-free way. To effectively fuse useful information from these two branches and acquire a better dehazed results, we propose an efficient feature aggregation block consisted of multiple parallel convolutions with different receptive. Moreover, we adopt a one-stage knowledge distillation strategy named online knowledge distillation to joint optimization of our OKDNet. The proposed OKDNet achieves superior performance compared with state-of-the-art methods on both synthetic and real-world images with fewer model parameters. Project website: https://github.com/lanyunwei/OKDNet.

MTUW-GAN: A Multi-Teacher Knowledge Distillation Generative Adversarial Network for Underwater Image Enhancement

“…In recent years, the rapid advancement of AI technology has propelled the rapid development of deep learning, which boasts powerful feature learning capabilities. Image processing methods based on deep learning have exhibited remarkable performance [12][13][14]. Consequently, numerous enhanced techniques have emerged within the domain of underwater image enhancement research [15][16][17][18][19][20].…”

“…In the domain of image dehazing, Lan et al [12] proposed the Online Knowledge Distillation Network (OKDNet) as a solution. This approach involves the construction of a multiscale feature extraction network utilizing residual dense blocks guided by an attention mechanism.…”