arXiv Computer Vision
Semantic-Guided Frequency Modulation for Learned Image Compression
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arXiv Computer Vision
Sep 1
Efficient JPEG Restoration in the Wavelet Domain via Mean Flows
arXiv:2608.28730v1 Announce Type: cross Abstract: Latest JPEG restoration systems achieve strong quality with large models, yet often remain too slow and expensive for efficient on-device deployment....
arXiv AI
1d ago
Neural Image Compression with Internal-External Prior Fusion and Alignment
The paper introduces Neural Image Compression with Internal-External Prior Fusion and Alignment (IE-PFA), a context model that combines internal priors from the current image with an external dictionary shared by encoder and decoder. It employs a RWKV-based Spatial-Channel Prior Fusion (RSCPF) module for efficient fusion and a Self-Distilled Prior Alignment (SDPA) mechanism for alignment during training. The model supports variable-rate coding across 64 quality levels, achieves a 21.65% BD-Rate saving over VTM-22.0-Intra, uses only 29.9M parameters, and offers fast encoding (61 ms) and decoding (52 ms) speeds.
arXiv Computer Vision
Oct 27
WAVE: Wavelet-Controlled Coarse-to-Fine Guidance for Depth Super-Resolution
arXiv:2608.25302v2 Announce Type: replace Abstract: Guided depth super-resolution (GDSR) typically extracts RGB guidance features through convolutional hierarchies, inheriting their fine-to-coarse bi...
arXiv Machine Learning
Aug 27
WAVE: Reversing the Guidance Hierarchy for Coarse-to-Fine Guided Depth Super-Resolution
WAVE introduces a multi-level discrete wavelet transform (ML‑DWT) to reverse the typical fine‑to‑coarse bias in guided depth super‑resolution. By consuming wavelet sub‑bands and semantic tokens in reverse order, it separates structure and detail reconstruction, applies semantic gating to high‑frequency bands, and fuses modalities via an invertible coupling mechanism. Experiments on multiple benchmarks show that WAVE matches or outperforms existing methods, especially at high upsampling factors where low‑resolution depth has minimal structure.
arXiv Computer Vision
Sep 15
MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery
MambaMPD is a new segmentation framework that leverages Vision Mamba models for marine pollution detection in remote‑sensing imagery. It introduces two structural priors—Frequency‑Aware Augmentation (FAA) and multi‑scale Edge‑Guided Attention (EGA)—to better capture low‑contrast, fragmented pollution patterns and sharpen boundaries. Experiments on the MADOS and M4D datasets show that MambaMPD outperforms existing methods in mIoU while using far less computation than foundation‑model approaches.
arXiv Machine Learning
Aug 21
Decoupling High and Low Frequencies for Faithful Image Generation with Fine Details
arXiv:2509. 05441v4 Announce Type: replace-cross Abstract: Latent generative models compress images into learned embeddings prior to synthesis, and the generation quality critically depends on how faithfully these embeddings preserve visual detail.