English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Salt and pepper noise removal is a common inverse problem in imageprocessing. Traditional denoising methods have two limitations. First, noisecharacteristics are often not described accurately. For example, the noiselocation information is often ignored and the sparsity of the salt and peppernoise is often described by L1 norm, which cannot illustrate the sparsevariables clearly. Second, conventional methods separate the contaminated imageinto a recovered image and a noise part, thus resulting in recovering an imagewith unsatisfied smooth parts and detail parts. In this study, we introduce anoise detection strategy to determine the position of the noise, and anon-convex sparsity regularization depicted by Lp quasi-norm is employed todescribe the sparsity of the noise, thereby addressing the first limitation.The morphological component analysis framework with stationary Framelettransform is adopted to decompose the processed image into cartoon, texture,and noise parts to resolve the second limitation. Then, the alternatingdirection method of multipliers (ADMM) is employed to solve the proposed model.Finally, experiments are conducted to verify the proposed method and compare itwith some current state-of-the-art denoising methods. The experimental resultsshow that the proposed method can remove salt and pepper noise while preservingthe details of the processed image.

Salt and pepper noise removal method based on stationary Framelet transform with non-convex sparsity regularization