Dictionary learning based image enhancement for rarity detection
Hui Li, Xiaomeng Wang, Weifeng Liu, Yanjiang Wang

TL;DR
This paper introduces a novel image enhancement technique using dictionary learning that manipulates the rarity of dictionary atoms, improving salient object extraction and aligning better with human visual perception.
Contribution
The paper presents a new dictionary learning-based image enhancement method that adjusts atom rarity for content-aware processing, surpassing traditional pixel distribution techniques.
Findings
Enhanced image quality for salient object detection
Better alignment with human visual response
Effective compared to traditional methods
Abstract
Image enhancement is an important image processing technique that processes images suitably for a specific application e.g. image editing. The conventional solutions of image enhancement are grouped into two categories which are spatial domain processing method and transform domain processing method such as contrast manipulation, histogram equalization, homomorphic filtering. This paper proposes a new image enhance method based on dictionary learning. Particularly, the proposed method adjusts the image by manipulating the rarity of dictionary atoms. Firstly, learn the dictionary through sparse coding algorithms on divided sub-image blocks. Secondly, compute the rarity of dictionary atoms on statistics of the corresponding sparse coefficients. Thirdly, adjust the rarity according to specific application and form a new dictionary. Finally, reconstruct the image using the updated…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Image Processing Techniques · Image Processing Techniques and Applications
