Fundamentals of digital image processing
Fundamentals of digital image processing
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Regularity-preserving image interpolation
IEEE Transactions on Image Processing
Image enhancement by nonlinear extrapolation in frequency space
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
Learning-Based Image Restoration for Compressed Image through Neighboring Embedding
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Learning-based image restoration for compressed images
Image Communication
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According to recent advances in digital image processing techniques, interest in high-quality images has been increased. This paper presents a resolution enhancement (RE) algorithm based on the pyramid structure, in which Laplacian histogram matching is utilized for high-frequency image prediction. The conventional RE algorithms yield blurring near-edge boundaries, degrading image details. In order to overcome this drawback, we estimate an HF image that is needed for RE by utilizing the characteristics of the Laplacian images, in which the normalized histogram of the Laplacian image is fitted to the Laplacian probability density function (pdf), and the parameter of the Laplacian pdf is estimated based on the Laplacian image pyramid. Also, we employ a control function to remove overshoot artifacts in reconstructed images. Experiments with several test images show the effectiveness of the proposed algorithm.