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Subjects / keywords: Computer science; Mathematics; Set theory; Problems, exercises, etc; Algorithms; Logic, Symbolic and mathematical; Problems and exercises; Electronic books

We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2-D image fragments (e.g., blocks) into 3-D data...

Subjects / keywords: TREMBLAY, JEAN-PAUL; MCGRAW-HILL; 005.113 T789in

Subjects / keywords: Python (Computer program language); Data structures (Computer science); Computer algorithms

Subjects / keywords: Text processing (Computer science); Genetic algorithms; Computational linguistics

Subjects / keywords: Computer algorithms; Databases; Data structures (Computer science); Structured programming

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construc...

We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial qual...