Resampling Images to a Regular Grid from a Non-Regular Subset of Pixel Positions Using Frequency Selective Reconstruction

Abstract

Even though image signals are typically defined on a regular two-dimensional grid, there also exist many scenarios where this is not the case and the amplitude of the image signal only is available for a non-regular subset of pixel positions. In such a case, a resampling of the image to a regular grid has to be carried out. This is necessary since almost all algorithms and technologies for processing, transmitting or displaying image signals rely on the samples being available on a regular grid. Thus, it is of great importance to reconstruct the image on this regular grid so that the reconstruction comes closest to the case that the signal has been originally acquired on the regular grid. In this paper, Frequency Selective Reconstruction is introduced for solving this challenging task. This algorithm reconstructs image signals by exploiting the property that small areas of images can be represented sparsely in the Fourier domain. By further taking into account the basic properties of the Optical Transfer Function of imaging systems, a sparse model of the signal is iteratively generated. In doing so, the proposed algorithm is able to achieve a very high reconstruction quality, in terms of PSNR and SSIM as well as in terms of visual quality. Simulation results show that the proposed algorithm is able to outperform state-of-the-art reconstruction algorithms and gains of more than 1 dB PSNR are possible.

References in corpus (5)

• Complex-Valued Frequency Selective Extrapolation for Fast Image and Video Signal Extrapolation
• Non-uniform sampling, image recovery from sparse data and the discrete sampling theorem
• Optimized and Parallelized Processing Order for Improved Frequency Selective Signal Extrapolation
• Orthogonality Deficiency Compensation for Improved Frequency Selective Image Extrapolation
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Cited by in corpus (12)

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• Increasing Imaging Resolution by Non-Regular Sampling and Joint Sparse Deconvolution and Extrapolation
• Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction
• Iterative Optimization of Quarter Sampling Masks for Non-Regular Sampling Sensors
• Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting
• Real-Time Frequency Selective Reconstruction through Register-Based Argmax Calculation
• Texture-Dependent Frequency Selective Reconstruction of Non-Regularly Sampled Images
• Recursive Frequency Selective Reconstruction of Non-Regularly Sampled Video Data
• Design Techniques for Incremental Non-Regular Image Sampling Patterns
• Fast Reconstruction of Three-Quarter Sampling Measurements Using Recurrent Local Joint Sparse Deconvolution and Extrapolation
• Enhanced Image Reconstruction From Quarter Sampling Measurements Using An Adapted Very Deep Super Resolution Network
• Novel Consistency Check For Fast Recursive Reconstruction Of Non-Regularly Sampled Video Data