Excavating Roi Attention For Underwater Object Detection

To reproduce the latent high-quality image from its observed image, traditional image restoration approaches based on low-rank prior usually solve the low-rank matrix approximation problem by minimizing the nuclear norm. However, most of approaches work on the desired singular values individually, and ignore the underlying statistical property of singular values. in this work, we propose a probability-inducing nuclear norm minimization (PinNM) algorithm, where a probability-inducing singular value estimator is presented to estimate the desired singular values. For further filling-in the image details lost in handling the singular values, a simple yet efficient residual cascade scheme is designed to refine the image quality by using the intermediate recovered image. Then, the proposed PinNM algorithm is applied on two classic image restoration tasks: image super-resolution and denoising. Experimental results demonstrate that, the proposed PinNM algorithm outperforms many state-of-the-art approaches both quantitatively and qualitatively for the aforementioned tasks. The source code is available at https://github.com/cvzh/PinNM.