Abstract
We study a multiscale tensor regularization based JPEG decompression artifact removal in digital images. Structure tensor eigenvalues based robust edge map is used within a variable exponent regularization. Variational constrained minimization which combines data fidelity driven by color subsampling and discrete cosine transformation operator is utilized. Experimental results across different compression levels and with various error metrics indicate our proposed method obtains high quality results on cartoon/clip-art and LIVE1 natural image databases.
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ARCNN method was run on the Y component (luminance) of the image converted to the YCbCr color space, and remaining methods were run using corresponding vectorial versions.
Compare to results from ARCNN (Dong et al. 2015).
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Surya Prasath, V.B., Thanh, D.N.H., Hieu, L.M. et al. Compression artifacts reduction with multiscale tensor regularization. Multidim Syst Sign Process 32, 521–531 (2021). https://doi.org/10.1007/s11045-020-00747-8
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DOI: https://doi.org/10.1007/s11045-020-00747-8