Improving a Single Down-Sampled Image Using Probability-Filtering-Based Interpolation and Improved Poisson Maximum A Posteriori Super-Resolution

Abstract

We present a novel hybrid scheme called "hyper-resolution" that integrates image probability-filtering-based interpolation and improved Poisson maximum a posteriori (MAP) super-resolution to respectively enhance high spatial and spatial-frequency resolutions of a single down-sampled image. A new approach to interpolation is proposed for simultaneous image interpolation and smoothing by exploiting the probability filter coupled with a pyramidal decomposition and the Poisson MAP super-resolution is improved with the techniques of edge maps and pseudo-blurring. Simulation results demonstrate that this hyper-resolution scheme substantially improves the quality of a single gray-level, color, or noisy image, respectively.

Image Restoration for Long-Wavelength Imaging Systems

“…4.1(b) is proposed for enhancing the spatial and the spatial-frequency resolutions of a down-sampled image. For more detailed description and examples, readers can refer to the previous work [Pan, 2006]. To illustrate the performance of this proposed scheme, comparisons are shown among the superresolution coupled with different interpolators as the following examples, Fig.…”

Image restoration through regularization based on error energy minimization

“…Since then, signal/image restoration has been concerned for the spatial-frequency domain from one lowresolution frame to one high-resolution frame; basically, the distinct nature of those algorithms is iterative and nonlinear. A process of interpolation along with restoration was used with one frame to enhance the spatial and spatial-frequency resolution of the frame (Pan, 2006). Furthermore, the processing of multiframes-to-oneframe has been quite concerned (Gillette et al, 1995;Ng et al, 2003;Segal et al, 2003), where a single high-resolution frame was reconstructed from multiple low-resolution frames.…”

Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images

“…Furthermore, Ntziachristos et al 33 and Intes et al 34 employed a fluorescent diagnostic agent, indocyanine green ͑ICG͒ to enhance heterogeneity contrast for obtaining better resolutions prior to optical image reconstruction. As to the background information about image processing techniques for the enhancement of reconstructed optical-property images, especially applied in this study, some monographs [35][36][37] and related papers [38][39][40] are valuable. Three referred to books are rather appropriate for the beginner, especially the third one, and three reference papers are actually the origin of the idea resulting in the proposed algorithm presented in this paper.…”