Adaptive and Robust Image Processing on CubeSats
- URL: http://arxiv.org/abs/2506.03152v1
- Date: Fri, 16 May 2025 16:03:04 GMT
- Title: Adaptive and Robust Image Processing on CubeSats
- Authors: Robert Bayer, Julian Priest, Daniel Kjellberg, Jeppe Lindhard, Nikolaj Sørenesen, Nicolaj Valsted, Ívar Óli, Pınar Tözün,
- Abstract summary: CubeSats offer a low-cost platform for space research, particularly for Earth observation.<n>This paper introduces two novel systems, DIPP and DISH, to address these challenges.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: CubeSats offer a low-cost platform for space research, particularly for Earth observation. However, their resource-constrained nature and being in space, challenge the flexibility and complexity of the deployed image processing pipelines and their orchestration. This paper introduces two novel systems, DIPP and DISH, to address these challenges. DIPP is a modular and configurable image processing pipeline framework that allows for adaptability to changing mission goals even after deployment, while preserving robustness. DISH is a domain-specific language (DSL) and runtime system designed to schedule complex imaging workloads on low-power and memory-constrained processors. Our experiments demonstrate that DIPP's decomposition of the processing pipelines adds negligible overhead, while significantly reducing the network requirements of updating pipelines and being robust against erroneous module uploads. Furthermore, we compare DISH to Lua, a general purpose scripting language, and demonstrate its comparable expressiveness and lower memory requirement.
Related papers
- RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation [51.37553739930992]
RPCANet++ is a sparse object segmentation framework that fuses the interpretability of RPCA with efficient deep architectures.<n>Our approach unfolds a relaxed RPCA model into a structured network comprising a Background Approximation Module (BAM), an Object Extraction Module (OEM) and an Image Restoration Module (IRM)<n>Experiments on diverse datasets demonstrate that RPCANet++ achieves state-of-the-art performance under various imaging scenarios.
arXiv Detail & Related papers (2025-08-06T08:19:37Z) - Efficient Multi-Instance Generation with Janus-Pro-Dirven Prompt Parsing [53.295515505026096]
Janus-Pro-driven Prompt Parsing is a prompt- parsing module that bridges text understanding and layout generation.<n>MIGLoRA is a parameter-efficient plug-in integrating Low-Rank Adaptation into UNet (SD1.5) and DiT (SD3) backbones.<n>The proposed method achieves state-of-the-art performance on COCO and LVIS benchmarks while maintaining parameter efficiency.
arXiv Detail & Related papers (2025-03-27T00:59:14Z) - Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing [50.92820394852817]
We propose a textitPrior-textitguided textitHarmonization Network (PGH$2$Net) for image dehazing.<n>PGH$2$Net is built upon the UNet-like architecture with an efficient encoder and decoder, consisting of two module types.
arXiv Detail & Related papers (2025-03-03T03:36:30Z) - DDU-Net: A Domain Decomposition-Based CNN for High-Resolution Image Segmentation on Multiple GPUs [46.873264197900916]
A domain decomposition-based U-Net architecture is introduced, which partitions input images into non-overlapping patches.<n>A communication network is added to facilitate inter-patch information exchange to enhance the understanding of spatial context.<n>Results show that the approach achieves a $2-3,%$ higher intersection over union (IoU) score compared to the same network without inter-patch communication.
arXiv Detail & Related papers (2024-07-31T01:07:21Z) - Parallel Cross Strip Attention Network for Single Image Dehazing [15.246376325081973]
Single image dehazing aims to restore hazy images and produce clear, high-quality visuals.
Traditional convolutional models struggle with long-range dependencies due to limited receptive field size.
We introduce a novel dehazing network based on Parallel Stripe Cross Attention (PCSA) with a multi-scale strategy.
arXiv Detail & Related papers (2024-05-09T14:50:07Z) - Look-Around Before You Leap: High-Frequency Injected Transformer for Image Restoration [46.96362010335177]
In this paper, we propose HIT, a simple yet effective High-frequency Injected Transformer for image restoration.
Specifically, we design a window-wise injection module (WIM), which incorporates abundant high-frequency details into the feature map, to provide reliable references for restoring high-quality images.
In addition, we introduce a spatial enhancement unit (SEU) to preserve essential spatial relationships that may be lost due to the computations carried out across channel dimensions in the BIM.
arXiv Detail & Related papers (2024-03-30T08:05:00Z) - Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures.
This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead.
We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
arXiv Detail & Related papers (2023-03-16T21:06:13Z) - LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing [19.74767410530179]
Deep unfolding networks (DUNs) have proven to be a viable approach to compressive sensing (CS)
In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS.
Our experiments on three widely considered datasets demonstrate the promising performance of LR-CSNet.
arXiv Detail & Related papers (2022-12-18T13:54:11Z) - Learning to Aggregate Multi-Scale Context for Instance Segmentation in
Remote Sensing Images [28.560068780733342]
A novel context aggregation network (CATNet) is proposed to improve the feature extraction process.
The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid ( SCP), and hierarchical region of interest extractor (HRoIE)
arXiv Detail & Related papers (2021-11-22T08:55:25Z) - PnP-DETR: Towards Efficient Visual Analysis with Transformers [146.55679348493587]
Recently, DETR pioneered the solution vision tasks with transformers, it directly translates the image feature map into the object result.
Recent transformer-based image recognition model andTT show consistent efficiency gain.
arXiv Detail & Related papers (2021-09-15T01:10:30Z) - ReconfigISP: Reconfigurable Camera Image Processing Pipeline [75.46902933531247]
Image Signal Processor (ISP) is crucial component in digital cameras that transforms sensor signals into images for us to perceive and understand.
Existing ISP designs always adopt a fixed architecture, e.g., several sequential modules connected in a rigid order.
In this study, we propose a novel Reconfigurable ISP (ReconfigISP) whose architecture and parameters can be automatically tailored to specific data and tasks.
arXiv Detail & Related papers (2021-09-10T09:56:43Z)
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.