Directional Analytic Discrete Cosine Frames
Seisuke Kyochi, Taizo Suzuki, Yuichi Tanaka

TL;DR
This paper introduces two novel directional analytic discrete cosine frames (DADCFs) for efficient sparse image representation, reducing computational complexity and memory use while maintaining rich directional features, outperforming traditional methods in image recovery.
Contribution
The paper proposes two new DADCFs based on DCT and DST that are non-overlapping, low redundancy, and suitable for high-resolution image processing, with improved efficiency and directional selectivity.
Findings
DADCFs require less computation and memory than conventional frames.
DADCFs demonstrate effective directional selectivity in image recovery.
Experimental results show superior performance of DADCFs over traditional transforms.
Abstract
Block frames called directional analytic discrete cosine frames (DADCFs) are proposed for sparse image representation. In contrast to conventional overlapped frames, the proposed DADCFs require a reduced amount of 1) computational complexity, 2) memory usage, and 3) global memory access. These characteristics are strongly required for current high-resolution image processing. Specifically, we propose two DADCFs based on discrete cosine transform (DCT) and discrete sine transform (DST). The first DADCF is constructed from parallel separable transforms of DCT and DST, where the DST is permuted by row. The second DADCF is also designed based on DCT and DST, while the DST is customized to have no DC leakage property which is a desirable property for image processing. Both DADCFs have rich directional selectivity with slightly different characteristics each other and they can be implemented…
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Taxonomy
TopicsOptical Coherence Tomography Applications · Image Processing Techniques and Applications · Image and Signal Denoising Methods
MethodsDynamic Sparse Training · Discrete Cosine Transform
