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Deep learning framework normalizes lunar imagery for seamless mosaics

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a deep learning framework to address radiometric inconsistencies in lunar mosaics created from different orbital imagery sources. The system utilizes a conditional generative adversarial network (cGAN) to map conventionally mosaicked images to a photometrically consistent reference. This approach, tested with Chandrayaan-2 TMC and SELENE data, significantly improves tonal uniformity and reduces seam artifacts compared to traditional methods. AI

IMPACT Enhances the fidelity of planetary surface maps by improving image mosaicking techniques.

RANK_REASON Academic paper presenting a novel deep learning framework for image processing.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep learning framework normalizes lunar imagery for seamless mosaics

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Pratincha Singh, Jai Gopal Singla, Prashant Hemrajani, Nitant Dube, Amithabh, Hinal Patel ·

    Towards Seamless Lunar Mosaics: Deep Radiometric Normalization for Cross-Sensor Orbital Imagery Using Chandrayaan-2 TMC Data

    arXiv:2604.25208v1 Announce Type: new Abstract: Radiometric inconsistencies remain a major challenge in generating seamless lunar mosaics from multi-mission orbital imagery due to variability in illumination geometry, sensor characteristics, and acquisition conditions. This paper…

  2. arXiv cs.CV TIER_1 English(EN) · Hinal Patel ·

    Towards Seamless Lunar Mosaics: Deep Radiometric Normalization for Cross-Sensor Orbital Imagery Using Chandrayaan-2 TMC Data

    Radiometric inconsistencies remain a major challenge in generating seamless lunar mosaics from multi-mission orbital imagery due to variability in illumination geometry, sensor characteristics, and acquisition conditions. This paper presents a deep learning-based radiometric norm…

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