Image denoising is a computer vision task that mainly aims to remove unwanted noise from a given image while preserving all necessary details and related information. Detection of edges and smooth image generation are required criteria for measuring the quality of the image denoiser. This paper introduces a new model for image denoising based on total variation (TV). The unprecedented novelty total variation (NTV) model combines norm-based total variation (TV) and norm-based TV regularization. This paper uses the implicit finite difference method to solve the NTV model numerically. The statistical measurements are used to compare the results obtained using the NTV model with those obtained using other models, which show the superiority of the proposed model in terms of its effectiveness and efficiency through removing different types of noise from images. This proposed model is effective in detail sharpening and texture preservation.
A Novel Total Variation Model for Image Denoising with Different Types of Noise
Image Denoising
Regularization Technique
Total Variation
Anisotropic Technique
Isotropic Technique
Implicit Finite Difference Method