Blind Multiple-Input Multiple-Output Image Phase Retrieval

Abstract

In this paper, we consider the problem of recovering the phase information of the multiple images from the multiple mixed phaseless Short-Time Fourier Transform (STFT) image measurements, which is called the blind multiple input multiple output image phase retrieval (BMIPR) problem. It is an inherently ill-posed problem due to the lack of the phase and mixing information, and the existing phase retrieval algorithms are not explicitly designed for this case. To address the BMIPR phase retrieval problem, an integrated algorithm is presented, which combines a gradient descent (GD) algorithm by minimizing a nonconvex loss function with an independent component analysis (ICA) algorithm and a non-local means (NM) algorithm. Experimental evaluation has been conducted to show that under appropriate conditions the proposed algorithms can explicitly recover the images, the phases of the images and the mixing matrix. In addition, the algorithm is robust to noise. Index Terms-Blind multiple input multiple output image phase retrieval (BMIPR), short-time Fourier transform (STFT), non-convex optimization, independent component analysis (ICA), non-local means (NM).

Joint Raindrop and Haze Removal From a Single Image

“…Our proposed algorithms in GPU are capable of dealing with a 480 × 640 image corrupted by raindrops and haze in less than 3s. For the experiments on synthetic images, the performance of the proposed algorithm can be evaluated by Structure Similarity Index (SSIM) [30] and Peak Signal-to-Noise Ratio (PSNR) [31], [32]. For the experiments on real-world images, the performance of the proposed algorithm can be evaluated by blind image quality index (BIQI) [33] and Blind referenceless image spatial quality evaluator (BRISQUE) [34].…”