In this paper, we propose a novel generalized low-rank tensor denoising model tailored for third-order tensors, designed to effectively address diverse types of noise in images and videos. The model integrates tensor Schatten p-norm and ℓ 1 -norm minimization into a unified and computationally efficient framework. To solve the resulting optimization problem, we employ the alternating direction method of multipliers, complemented by a generalized soft-thresholding algorithm for efficient subproblem computation. Extensive experiments on image and video datasets demonstrate that the proposed method consistently outperforms classical tensor-based algorithms, highlighting its effectiveness and potential for practical applications in image and video processing.