Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search

• URL: http://arxiv.org/abs/2405.19112v1
• Date: Wed, 29 May 2024 14:20:46 GMT
• Title: Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search
• Authors: Marzieh Gheisari, Auguste Genovesio,
• Abstract summary: Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. Recent methods based on GAN latent search offered a drastic increase in resolution without the need of paired images. Here, we propose a robust super-resolution method based on regularized latent search(RLS) that offers an actionable balance between fidelity to the ground-truth and realism of the recovered image.
• Score: 2.7194314957925094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based on GAN latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution image interpretable features. Here, we propose a robust super-resolution method based on regularized latent search~(RLS) that offers an actionable balance between fidelity to the ground-truth and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution image into a computational super-resolution task performed by deep learning followed by a quantification task performed by a handcrafted algorithm and based on interpretable biological features. This two-step process holds potential for various applications such as diagnostics on mobile devices, where the main aim is not to recover the high-resolution details of a specific sample but rather to obtain high-resolution images that preserve explainable and quantifiable differences between conditions.

Related papers

• Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment [6.350679043444348]
  Histo-Diffusion is a novel diffusion-based method specially designed for generating and evaluating super-resolution images in digital pathology. It includes a restoration module for histopathology prior and a controllable diffusion module for generating high-quality images.
  arXiv  Detail & Related papers  (2024-08-27T17:31:00Z)
• URCDM: Ultra-Resolution Image Synthesis in Histopathology [4.393805955844748]
  Ultra-Resolution Cascaded Diffusion Models (URCDMs) are capable of synthesising entire histopathology images at high resolutions. We evaluate our method on three separate datasets, consisting of brain, breast and kidney tissue. URCDMs consistently generate outputs across various resolutions that trained evaluators cannot distinguish from real images.
  arXiv  Detail & Related papers  (2024-07-18T08:31:55Z)
• Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representations [23.04458119996]
  We propose a novel continuous low-rank factorization (CLoRF) by integrating two neural representations into the matrix factorization. This approach enables us to harness both the low rankness from the matrix factorization and the continuity from neural representation in a self-supervised manner.
  arXiv  Detail & Related papers  (2024-05-28T04:29:23Z)
• Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion [60.221404321514086]
  Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. This paper presents a Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which introduces a bi-level optimization search scheme for automatic design of both network structures and loss functions.
  arXiv  Detail & Related papers  (2023-09-03T08:07:26Z)
• Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification [49.57112924976762]
  Cross-resolution person re-identification problem aims to match low-resolution (LR) query identity images against high resolution (HR) gallery images. It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras. This paper explores an alternative SR-free paradigm to directly compare HR and LR images via a dynamic metric, which is adaptive to the resolution of a query image.
  arXiv  Detail & Related papers  (2022-07-09T03:49:51Z)
• Image Matching with Scale Adjustment [57.18604132027697]
  We show how to represent and extract interest points at variable scales. We devise a method allowing the comparison of two images at two different resolutions.
  arXiv  Detail & Related papers  (2020-12-10T11:03:25Z)
• Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition [7.6702700993064115]
  We propose an identity-preserving end-to-end image-to-image translation deep neural network. It is capable of super-resolving very low-resolution faces to their high-resolution counterparts while preserving identity-related information.
  arXiv  Detail & Related papers  (2020-10-23T09:21:06Z)
• Invertible Image Rescaling [118.2653765756915]
  We develop an Invertible Rescaling Net (IRN) to produce visually-pleasing low-resolution images. We capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
  arXiv  Detail & Related papers  (2020-05-12T09:55:53Z)
• Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces [7.634398926381845]
  Super-resolution method is often used to enhance low-resolution images, but the performance on FER task is limited when on images of very low resolution. In this work, inspired by feature super-resolution methods for object detection, we proposed a novel generative adversary network-based super-resolution method for robust facial expression recognition.
  arXiv  Detail & Related papers  (2020-04-05T15:38:47Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)
• Gated Fusion Network for Degraded Image Super Resolution [78.67168802945069]
  We propose a dual-branch convolutional neural network to extract base features and recovered features separately. By decomposing the feature extraction step into two task-independent streams, the dual-branch model can facilitate the training process.
  arXiv  Detail & Related papers  (2020-03-02T13:28:32Z)

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.