Solving Inverse Computational Imaging Problems Using Deep Pixel-Level Prior

Dave, Akshat; Vadathya, Anil Kumar; Subramanyam, Ramana; Baburajan, Rahul; Mitra, Kaushik

doi:10.1109/tci.2018.2882698

Cited by 24 publications

(20 citation statements)

References 24 publications

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“…For compressive MRI recovery, we used PnP ADMM from (13) with f (·) as the CNN-based denoiser described above; we will refer to the combination as PnP-CNN. We employed a total of 100 ADMM iterations, and in each ADMM iteration, we performed four steps of CG to approximate (12), for which we used σ 2 = 1 = η.…”

Section: Demonstration Of Pnp In Mri a Parallel Cardiac Mrimentioning

confidence: 99%

“…Also, since CNN training occurs in the presence of dataset-specific forward models, generalization from training to test scenarios remains an open question [9]. Other learning-based methods have been proposed based on bi-level optimization (e.g., [10]), adversarially learned priors (e.g., [12]), and autoregressive priors (e.g., [13]). Consequently, the integration of learning-based methods into physical inverse problems remains a fertile area of research.…”

Section: Introductionmentioning

confidence: 99%

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Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery

Ahmad

Bouman

Buzzard

et al. 2020

IEEE Signal Process. Mag.

218

135

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Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging modalities (e.g., CT or ultrasound), however, the data acquisition process for MRI is inherently slow, which motivates undersampling and thus drives the need for accurate, efficient reconstruction methods from undersampled datasets. In this article, we describe the use of "plug-and-play" (PnP) algorithms for MRI image recovery. We first describe the linearly approximated inverse problem encountered in MRI. Then we review several PnP methods, where the unifying commonality is to iteratively call a denoising subroutine as one step of a larger optimizationinspired algorithm. Next, we describe how the result of the PnP method can be interpreted as a solution to an equilibrium equation, allowing convergence analysis from the equilibrium perspective. Finally, we present illustrative examples of PnP methods applied to MRI image recovery.

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Section: Demonstration Of Pnp In Mri a Parallel Cardiac Mrimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery

Ahmad

Bouman

Buzzard

et al. 2020

IEEE Signal Process. Mag.

218

135

View full text Add to dashboard Cite

show abstract

“…A class of deep learning based solution involves learning of regularizers or proximal mapping stage and then iteratively solving a MAP problem. Methods like [21], [22], [23] fall under this category. Another class of algorithm is designed as a feed-forward deep neural network that has either been trained in a supervised or self-supervised manner.…”

Section: Image Reconstructionmentioning

confidence: 99%

FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

Khan

Sundar

Boominathan

et al. 2020

IEEE Trans. Pattern Anal. Mach. Intell.

Self Cite

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Lensless imaging has emerged as a potential solution towards realizing ultra-miniature cameras by eschewing the bulky lens in a traditional camera. Without a focusing lens, the lensless cameras rely on computational algorithms to recover the scenes from multiplexed measurements. However, the current iterative-optimization-based reconstruction algorithms produce noisier and perceptually poorer images. In this work, we propose a non-iterative deep learning-based reconstruction approach that results in orders of magnitude improvement in image quality for lensless reconstructions. Our approach, called FlatNet, lays down a framework for reconstructing high-quality photorealistic images from mask-based lensless cameras, where the camera's forward model formulation is known. FlatNet consists of two stages: (1) an inversion stage that maps the measurement into a space of intermediate reconstruction by learning parameters within the forward model formulation, and (2) a perceptual enhancement stage that improves the perceptual quality of this intermediate reconstruction. These stages are trained together in an end-to-end manner. We show high-quality reconstructions by performing extensive experiments on real and challenging scenes using two different types of lensless prototypes: one which uses a separable forward model and another, which uses a more general non-separable cropped-convolution model. Our end-to-end approach is fast, produces photorealistic reconstructions, and is easy to adopt for other mask-based lensless cameras.

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“…where θ is a vector of parameters that controls both the penalty function φ and the analysis filters, L. Regularizers of other forms can also be learned, e.g., [63] use a pixel-wise autoregressive model as a regularizer.…”

Section: Learning the Regularization Termmentioning

confidence: 99%