ROP - Rank-One Prior: Real-time Scene Recovery. Description. The Code is created based on the method described in the following papers: [1] "Rank-One Prior: Toward Real-Time Scene Recovery", Jun Liu, Ryan Wen Liu, Jianing Sun, Tieyong Zeng, IEEE International Conference on Computer Vision (CVPR), 2021, 14802-14810. ...

2013年10月22日 · In this paper, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case.

2021年3月31日 · The heart of our work is that we propose an intensity projection strategy to estimate the transmission. This strategy is motivated by a straightforward rank-one transmission prior. The complexity of transmission estimation is $O(N)$ where $N$ is the size of the single image. Then we can recover the scene in real-time.

In this paper, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is adaptive to the rank and robust against small pertur-bations.

Mathematically, we can introduce a rank-one matrix to characterize this phenomenon, i.e., rank-one prior (ROP). Using the prior, a direct method with the complexity O(N) is derived for real-time recovery. For general cases, we develop ROP + to further improve the recovery performance.

The Royal Oman Police (ROP) (Arabic: شرطة عمان السلطانية), also known as Oman Police, is the main law and order agency for the Sultanate of Oman. The concept of a modern police force was relatively new to Oman when Sultan Qaboos bin Said al Said rose to power after overthrowing his father in a palace coup on July 23, 1970.

2023年12月20日 · Mathematically, we can introduce a rank-one matrix to characterize this phenomenon, i.e., rank-one prior (ROP). Using the prior, a direct method with the complexity O(N) is derived for...

In this paper, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is adaptive to the rank and robust against small low-rank perturbations.

Rank-One Prior implemented in Python. The code is created for dehaze, sand dust image and underwater image enhancement.

In this paper, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is adaptive to the rank and robust against small perturbations.