Combining CBAM and Iterative Shrinkage-Thresholding Algorithm for Compressive Sensing of Bird Images

Bird research contributes to understanding species diversity, ecosystem functions, and the maintenance of biodiversity. By analyzing bird images and the audio of birds, we can monitor bird distribution, abundance, and behavior to better understand the health of ecosystems. However, bird images and audio involve a vast amount of data. To improve the efficiency of data transmission and storage efficiency and save bandwidth, compressive sensing can overcome this challenge. Compressive sensing is a technique that uses the sparsity of signals to recover original data from a small number of linear measurements. This paper introduces a deep neural network based on the Iterative Shrinkage Thresholding Algorithm (ISTA) and a Convolutional Block Attention Module (CBAM), CBAM_ISTA-Net(+), for the compressive reconstruction of bird images, audio Mel spectrograms and wavelet transform spectrograms. Using 45 bird species as research subjects, including 20 bird images, 15 audio-generated Mel spectrograms, and 10 audio wavelet transform (WT) spectrograms, the experimental results show that CBAM_ISTA-Net(+) achieves a higher peak signal-to-noise ratio (PSNR) at different compression ratios. At a compression ratio of 50%, the average PSNR of the three datasets reaches 33.62 dB, 55.76 dB, and 38.59 dB, while both the Mel spectrogram and wavelet transform spectrogram achieve more than 30 dB at compression ratios of 25-50%. These results highlight the effectiveness of CBAM_ISTA-Net(+) in maintaining high reconstruction quality even under significant compression, demonstrating its potential as a valuable tool for efficient data management in ecological research.