Enhanced Deconvolution and Denoise Method for Scattering Image Restoration - Zepeng Chen; Haolin Wu; Wenyong Li; Jiahui Wang (Photonics, 2023)
Light scattering is a common physical phenomenon in nature. The scattering medium will randomly change the direction of incident light propagation, making it difficult for traditional optical imaging methods to detect objects behind the scattering body. Wiener filtering deconvolution technology based on the optical memory effect has broad application prospects by virtue of its advantages, such as fast calculation speed and low cost. However, this method requires manual parameter adjustment, which is inefficient and cannot deal with the impact of real-scene noise. This paper proposes an improved Wiener filtering deconvolution method that improves the exposure dose during the speckle collection, can quickly obtain the optimal parameter during the calculation phase, and can be completed within 41.5 ms (for a 2448 × 2048 image). In addition, a neural network denoising model was proposed to address the noise issue in the deconvolution recovery results, resulting in an average improvement of 27.3% and 186.7% in PSNR and SSIM of the images, respectively. The work of this paper will play a role in achieving real-time high-quality imaging of scattering media and be helpful in studying the physical mechanisms of scattering imaging.