Wavelet-Bilinear Super-Resolution Reconstruction with Local Adaptive Interpolation
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Resource Overview
The conventional wavelet-bilinear super-resolution reconstruction method often suffers from mismatched low-frequency and high-frequency coefficients, leading to grayscale deviations in the resulting high-resolution image. This paper introduces an improved approach by incorporating local adaptive interpolation, resulting in a more robust reconstruction algorithm—specifically, a wavelet-local adaptive interpolation hybrid method. The enhanced algorithm aligns coefficient distributions through pixel-adaptive weighting and interpolation kernels, reducing reconstruction artifacts.
Detailed Documentation
When employing the wavelet-bilinear super-resolution reconstruction algorithm, a common issue arises from the mismatch between low-frequency and high-frequency coefficients, which causes grayscale shifts in the high-resolution output. To address this limitation, we propose an enhanced method that integrates local adaptive interpolation for superior reconstruction performance. By combining wavelet transformation with local adaptive interpolation techniques, our novel algorithm achieves improved alignment of low- and high-frequency coefficients, yielding more accurate high-resolution images. Key implementation aspects include adaptive kernel sizing based on local gradient features and weighted interpolation to preserve edge details. Notably, this approach not only enhances reconstructed image quality but also minimizes reconstruction errors through optimized frequency-domain consistency.
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