Papers by EE20D501 KRISHNA SUMANTH VENGALA
IEEE Signal Processing Letters, 2022
Phase unwrapping is a challenging task in signal processing, spanning its applications in optical... more Phase unwrapping is a challenging task in signal processing, spanning its applications in optical metrology, SAR interferometry, and many other signal reconstruction tasks. Fringe Projection Profilometry is a popular active-sensing approach for generating high-resolution three-dimensional (3D) surface information in which phase unwrapping is a crucial step. This letter proposes a multi-task learning-based phase unwrapping method for simultaneous denoising and wrap-count prediction in fringe projection. The proposed network, referred to as TriNet, has nested pyramidal architecture with a single encoder and two decoders, all connected through skip connections. The proposed approach does not require any pre-processing for noise removal like the conventional methods or any post-processing such as smoothing, like in existing deep learning methods but results in a quite accurate phase unwrapping. The proposed method outperforms the existing and state-of-the-art methods for the 3D reconstruction task in Fringe Projection by a significant margin even in the presence of very high noise.
2020 IEEE International Conference on Image Processing (ICIP), 2020
Phase reconstruction in Digital Holographic Interferometry (DHI) is widely employed for 3D deform... more Phase reconstruction in Digital Holographic Interferometry (DHI) is widely employed for 3D deformation measurements of the object surfaces. The key challenge in phase reconstruction in DHI is in the estimation of the absolute phase from noisy reconstructed interference fringes. In this paper, we propose a novel efficient deep learning approach for the phase estimation from noisy interference fringes in DHI. The proposed approach takes noisy reconstructed interference fringes as input and estimates the 3D deformation field or the object surface profile as the output. The 3D deformation field measurement of the object is posed as the absolute phase estimation from the noisy wrapped phase, that can be obtained from the reconstructed interference fringes through arctan function. The proposed deep neural network is trained to predict the fringe-order through a fully convolutional semantic segmentation network, from the noisy wrapped phase. These predictions are improved by simultaneously minimizing the regression error between the true phase corresponding to the object deformation field and the estimated absolute phase considering the predicted fringe order. We compare our method with conventional methods as well as with the recent state-of-the-art deep learning phase unwrapping methods. The proposed method outperforms conventional approaches by a large margin, while we can observe significant improvement even with respect to recently proposed deep learning-based phase unwrapping methods, in the presence of noise as high as 0dB to -5dB.
Computer Vision and Image Understanding, 2020
Journal of the Optical Society of America A, 2021
The extraction of absolute phase from an interference pattern is a key step for 3D deformation me... more The extraction of absolute phase from an interference pattern is a key step for 3D deformation measurement in digital holographic interferometry (DHI) and is an ill-posed problem. Estimating the absolute unwrapped phase becomes even more challenging when the obtained wrapped phase from the interference pattern is noisy. In this paper, we propose a novel multitask deep learning approach for phase reconstruction and 3D deformation measurement in DHI, referred to as TriNet, that has the capability to learn and perform two parallel tasks from the input image. The proposed TriNet has a pyramidal encoder–two-decoder framework for multi-scale information fusion. To our knowledge, TriNet is the first multitask approach to accomplish simultaneous denoising and phase unwrapping of the wrapped phase from the interference fringes in a single step for absolute phase reconstruction. The proposed architecture is more elegant than recent multitask learning methods such as Y-Net and state-of-the-art...
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Papers by EE20D501 KRISHNA SUMANTH VENGALA