Mixed l0/l1 Norm Minimization Approach to Super-Resolution
نویسندگان
چکیده
This deals with the problem of recovering a high-resolution digital image from one low resolution digital image and proposes a super-resolution algorithm based on the mixed l0/l1 norm minimization. Introducing some assumptions and focusing the uniformity and the gradation of the image, this paper formulates the colorization problem as a mixed l0/l1 norm minimization and proposes the algorithm based on the iterative reweighted least squares (IRLS) [1]. Numerical examples show that the proposed algorithm recovers a super-resolution image efficiently.
منابع مشابه
Image Representation Using a Sparsely Sampled Codebook for Super-Resolution
In this chapter, the authors propose a Super-Resolution (SR) method using a vector quantization codebook and filter dictionary. In the process of SR, we use the idea of compressive sensing to represent a sparsely sampled signal under the assumption that a combination of a small number of codewords can represent an image patch. A low-resolution image is obtained from an original high-resolution ...
متن کاملStable Image Colorization Algorithm Based on the Mixed L0/L1 Norm Minimization
This paper proposes a colorization algorithm based on the mixed L0/L1 norm minimization. Authors have already proposed a colorization algorithm, however, it requires appropriate parameters, and its performance highly depends on these parameters. This paper introduces some heuristic and modifies the algorithm in order to reduce the dependence of parameters. Numerical examples show that the propo...
متن کاملAN adaptive L1-L2 hybrid error model to super-resolution
A hybrid error model with L1 and L2 norm minimization criteria is proposed in this paper for image/video super-resolution. A membership function is defined to adaptively control the tradeoff between the L1 and L2 norm terms. Therefore, the proposed hybrid model can have the advantages of both L1 norm minimization (i.e. edge preservation) and L2 norm minimization (i.e. smoothing noise). In addit...
متن کاملA Hybrid L0-L1 Minimization Algorithm for Compressed Sensing MRI
INTRODUCTION Both L1 minimization [1] and homotopic L0 minimization [2] techniques have shown success in compressed-sensing MRI reconstruction using reduced k-space data. L1 minimization algorithm is known to usually shrink the magnitude of reconstructions especially for larger coefficients [1, 3] and non-convex penalty used in homotopic L0 minimization is advocated to replace L1 penalty [3]. H...
متن کاملA Hybrid L0-L1 Minimization Algorithm for Compressed Sensing MRI
INTRODUCTION Both L1 minimization [1] and homotopic L0 minimization [2] techniques have shown success in compressed-sensing MRI reconstruction using reduced k-space data. L1 minimization algorithm is known to usually shrink the magnitude of reconstructions especially for larger coefficients [1, 3] and non-convex penalty used in homotopic L0 minimization is advocated to replace L1 penalty [3]. H...
متن کامل