Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach

• URL: http://arxiv.org/abs/2406.17970v1
• Date: Tue, 25 Jun 2024 23:03:48 GMT
• Title: Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach
• Authors: Leon Suarez-Rodriguez, Roman Jacome, Henry Arguello,
• Abstract summary: This paper proposes a knowledge distillation (KD) framework for the design of highly physically constrained COI systems. We validate the proposed approach, using a binary coded apertures single pixel camera for monochromatic and multispectral image reconstruction.
• Score: 15.662108754691864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational optical imaging (COI) systems have enabled the acquisition of high-dimensional signals through optical coding elements (OCEs). OCEs encode the high-dimensional signal in one or more snapshots, which are subsequently decoded using computational algorithms. Currently, COI systems are optimized through an end-to-end (E2E) approach, where the OCEs are modeled as a layer of a neural network and the remaining layers perform a specific imaging task. However, the performance of COI systems optimized through E2E is limited by the physical constraints imposed by these systems. This paper proposes a knowledge distillation (KD) framework for the design of highly physically constrained COI systems. This approach employs the KD methodology, which consists of a teacher-student relationship, where a high-performance, unconstrained COI system (the teacher), guides the optimization of a physically constrained system (the student) characterized by a limited number of snapshots. We validate the proposed approach, using a binary coded apertures single pixel camera for monochromatic and multispectral image reconstruction. Simulation results demonstrate the superiority of the KD scheme over traditional E2E optimization for the designing of highly physically constrained COI systems.

Related papers

• Global Search Optics: Automatically Exploring Optimal Solutions to Compact Computational Imaging Systems [15.976326291076377]
  The popularity of mobile vision creates a demand for advanced compact computational imaging systems. Joint design pipelines come to the forefront, where the two significant components are simultaneously optimized via data-driven learning. In this work, we present Global Search Optimization (GSO) to design compact computational imaging systems.
  arXiv  Detail & Related papers  (2024-04-30T01:59:25Z)
• ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System for Image Classification [63.49762079000726]
  We propose a convolutional broad learning system (ConvBLS) based on the spherical K-means (SKM) algorithm and two-stage multi-scale (TSMS) feature fusion. Our proposed ConvBLS method is unprecedentedly efficient and effective.
  arXiv  Detail & Related papers  (2023-04-01T04:16:12Z)
• Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising [50.039949798156826]
  This paper tackles the challenging problem of hyperspectral (HS) image denoising. We propose rank-enhanced low-dimensional convolution set (Re-ConvSet) We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method.
  arXiv  Detail & Related papers  (2022-07-09T13:35:12Z)
• Deep Optical Coding Design in Computational Imaging [16.615106763985942]
  Computational optical imaging (COI) systems leverage optical coding elements (CE) in their setups to encode a high-dimensional scene in a single or multiple snapshots and decode it by using computational algorithms. The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task. Deep neural networks (DNNs) have opened a new horizon in CE data-driven designs that jointly consider the optical encoder and computational decoder.
  arXiv  Detail & Related papers  (2022-06-27T04:41:48Z)
• D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion [22.0246327137227]
  This paper presents the fusion of the compressive measurements of a low-spatial high-spectral resolution coded aperture snapshot spectral imager (CASSI) architecture and a high-spatial low-spectral resolution multispectral color filter array (MCFA) system. Unlike previous CSIF works, this paper proposes joint optimization of the sensing architectures and a reconstruction network in an end-to-end (E2E) manner.
  arXiv  Detail & Related papers  (2022-05-24T15:39:34Z)
• Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image Recognition [124.80263629921498]
  We propose Pixel Distillation that extends knowledge distillation into the input level while simultaneously breaking architecture constraints. Such a scheme can achieve flexible cost control for deployment, as it allows the system to adjust both network architecture and image quality according to the overall requirement of resources.
  arXiv  Detail & Related papers  (2021-12-17T14:31:40Z)
• Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement [58.72667941107544]
  A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces. We develop a new context-sensitive decomposition network architecture to exploit the scene-level contextual dependencies on spatial scales. We develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels.
  arXiv  Detail & Related papers  (2021-12-09T06:25:30Z)
• Image-specific Convolutional Kernel Modulation for Single Image Super-resolution [85.09413241502209]
  In this issue, we propose a novel image-specific convolutional modulation kernel (IKM) We exploit the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels. Experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-16T11:05:10Z)
• End to end hyperspectral imaging system with coded compression imaging process [13.00211539170695]
  We present a physics-informed self-supervising CNN method based on a coded aperture spectral imaging system. Our method effectively exploits the spatial-spectral relativization from the coded spectral information and forms a self-supervising system based on the camera quantum effect model.
  arXiv  Detail & Related papers  (2021-09-06T13:39:54Z)
• Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems [56.154190098338965]
  We show that our proposed time multiplexed coded aperture (TMCA) can be optimized end-to-end. TMCA induces better coded snapshots enabling superior reconstructions in two different applications: compressive light field imaging and hyperspectral imaging. This codification outperforms the state-of-the-art compressive imaging systems by more than 4dB in those applications.
  arXiv  Detail & Related papers  (2021-04-06T22:42:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.