Dictionary learning based image enhancement for rarity detection

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 dictionary and sparse coefficients. Compared with the traditional techniques, the proposed method enhances image based on the image content not on distribution of pixel grey value or frequency. The advantages of the proposed method lie in that it is in better correspondence with the response of the human visual system and more suitable for salient objects extraction. The experimental results demonstrate the effectiveness of the proposed image enhance method.