Dictionary Construction Method for Image Training

In this article, we present a dictionary construction method based on image training. This approach can be utilized for constructing basis matrices in compressed sensing. Compressed sensing is a signal processing technique that reduces the amount of data required for transmission or storage while maintaining high quality. This technology has found widespread applications in image and video processing, sensor networks, and communication systems. However, to implement compressed sensing, one must construct a basis matrix that represents signals as linear combinations. Here, we introduce a novel construction method that leverages image training to generate more efficient basis matrices, consequently improving compressed sensing performance. The implementation typically involves training algorithms that optimize dictionary atoms using image patches, with common approaches including K-SVD or online dictionary learning methods that iteratively update the dictionary to better represent training data.