Currently

most of the color demosaicking algorithms using only a local spatial and spectral correlation

easily lead restored picture to blur edges and loss of fine structures.When cyclical small structures exist in the image

these local methods are prone to the distortion of the zipper effect

the raster effect

etc.To solve these problems

unifying dictionary learning and sparse coding into a variational framework

a non-local adaptive sparse representation model is proposed through non-local similarity clustering and adaptive dictionary online learning.Using the local and non-local redundancy

sparse coding constraints forced sparse coding close to its non-local means to reduce coding errors.Moreover

the fidelity term is characterized by l

1

-norm to suppress the heavy-tailed vi

sual artifacts.Finally

the joint alternating minimization method and operator splitting techniques are utilized to effectively solve the model.Experimental results demonstrate the effectiveness of the proposed model and the numerical algorithm.