Image enhancement using Retinex and image fusion techniques

Yamakawa, Masaya; Sugita, Yasunori

doi:10.1002/ecj.12092

Cited by 22 publications

(14 citation statements)

References 16 publications

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“…Le and Li [215] improved the contrast of an image by fusing the original image with the image obtained after a logarithmic transformation. Yamakawa and Sugita presented an image fusion technique that used a source image and the Retinexprocessed image to achieve high visibility in both bright and dark areas [216]. In Ref.…”

Section: ) Fusion Based On a Single Imagementioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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Section: ) Fusion Based On a Single Imagementioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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“…Enhancement strategies, such as the traditional histogram equalization method, can reduce the contrast of low-frequency images and cause the loss of some energy information. Therefore, for a low-frequency image S 0 k , the image enhancement method based on SSR is used in the present study [19]. The SSR theory method can be described as follows.…”

Section: Enhancement Of Low-frequency Sub-band Coefficientsmentioning

confidence: 99%

An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement

2018

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Based on the advantages of a non-subsampled shearlet transform (NSST) in image processing and the characteristics of remote sensing imagery, NSST was applied to enhance blurred images. In the NSST transform domain, directional information measurement can highlight textural features of an image edge and reduce image noise. Therefore, NSST was applied to the detailed enhancement of high-frequency sub-band coefficients. Based on the characteristics of a low-frequency image, the retinex method was used to enhance low-frequency images. Then, an NSST inverse transformation was performed on the enhanced low- and high-frequency coefficients to obtain an enhanced image. Computer simulation experiments showed that when compared with a traditional image enhancement strategy, the method proposed in this paper can enrich the details of the image and enhance the visual effect of the image. Compared with other algorithms listed in this paper, the brightness, contrast, edge strength, and information entropy of the enhanced image by this method are improved. In addition, in the experiment of noisy images, various objective evaluation indices show that the method in this paper enhances the image with the least noise information, which further indicates that the method can suppress noise while improving the image quality, and has a certain level of effectiveness and practicability.

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“…The Harr wavelet response value dx and dy are added to form a new vector in this sector region, and the whole circular area is traversed to select the direction of the longest vector as the principal director of this feature point. Therefore, the principal direction of each feature point can be obtained by computing candidate feature points one by one, as shown in Equation (12), and the blobs detection is shown in Figure 2.…”

Section: Identification Of Principal Directionmentioning

confidence: 99%

“…Image fusion belongs to data information fusion and indicates the information processing to automatically analyze and integrate images. This is obtained under different conditions, according to certain rules by using computer image processing technology and completing the required decision and estimation tasks [12]. The corresponding images can be converted to identify the overlapping regions among images and map the fused images to a new blank image, forming the mosaic image.…”

Section: Image Fusionmentioning

confidence: 99%

Image Stitching in Smog Weather based on MSR and SURF

Li¹

2018

IJPE

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Image stitching can enlarge the range of viewing angles and increase different images information, and it is used in many fields such as industry, civil, and military. However, smog weather is an environmental problem in our country, because it can cause serious degradation of images. The loss of characteristic information will have negative impacts on the subsequent stitching process. Firstly, the smog image should be improved. In this paper, the application of the Multi-Scale Retinex (MSR) algorithm and the comparison and objective evaluation between it and the Histogram Equalization (HE) is discussed. Then, after removing the smog, the image is registered using local invariant features and the Speeded-up Robust Features (SURF) algorithm, and the Euclidean distance is adopted to obtain a satisfactory matching. Finally, the image stitching after registration may produce discontinuity of brightness in the overlapping area, and a higher quality stitching image can be achieved more quickly by using the progressive fade-out method. Through experiments and simulations, the smog images could be well stitched after removing the smog.

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Image enhancement using Retinex and image fusion techniques

Cited by 22 publications

References 16 publications

An Experiment-Based Review of Low-Light Image Enhancement Methods

An Experiment-Based Review of Low-Light Image Enhancement Methods

An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement

Image Stitching in Smog Weather based on MSR and SURF

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