Compressed Adaptive-Sampling-Rate Image Sensing Based on Overcomplete Dictionary

TL;DR

This paper introduces a new image sensing method that uses an overcomplete ridgelet dictionary to improve image reconstruction quality while keeping computation simple.

Contribution

The novel approach uses an overcomplete ridgelet dictionary and adaptive sampling without relying on the original signal.

Findings

The method achieves efficient sparse image representation with improved reconstruction quality.

It maintains low computational complexity, suitable for devices with limited computing power.

Experiments show better performance than existing ARCS methods.

Abstract

In this paper, a compressed adaptive image-sensing method based on an overcomplete ridgelet dictionary is proposed. Some low-complexity operations are designed to distinguish between smooth blocks and texture blocks in the compressed domain, and adaptive sampling is performed by assigning different sampling rates to different types of blocks. The efficient, sparse representation of images is achieved by using an overcomplete ridgelet dictionary; at the same time, a reasonable dictionary-partitioning method is designed, which effectively reduces the number of candidate dictionary atoms and greatly improves the speed of classification. Unlike existing methods, the proposed method does not rely on the original signal, and computation is simple, making it particularly suitable for scenarios where a device’s computing power is limited. At the same time, the proposed method can accurately identify smooth image blocks and more reasonably allocate sampling rates to obtain a reconstructed image with better quality. The experimental results show that our method’s image reconstruction quality is superior to that of existing ARCS methods and still maintains low computational complexity.

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