169 research outputs found
Retinex theory for color image enhancement: A systematic review
A short but comprehensive review of Retinex has been presented in this paper. Retinex theory aims to explain human color perception. In addition, its derivation on modifying the reflectance components has introduced effective approaches for images contrast enhancement. In this review, the classical theory of Retinex has been covered. Moreover, advance and improved techniques of Retinex, proposed in the literature, have been addressed. Strength and weakness aspects of each technique are discussed and compared. An optimum parameter is needed to be determined to define the image degradation level. Such parameter determination would help in quantifying the amount of adjustment in the Retinex theory. Thus, a robust framework to modify the reflectance component of the Retinex theory can be developed to enhance the overall quality of color images
Physical-based optimization for non-physical image dehazing methods
Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods
Perceiving Unknown in Dark from Perspective of Cell Vibration
Low light very likely leads to the degradation of image quality and even
causes visual tasks' failure. Existing image enhancement technologies are prone
to over-enhancement or color distortion, and their adaptability is fairly
limited. In order to deal with these problems, we utilise the mechanism of
biological cell vibration to interpret the formation of color images. In
particular, we here propose a simple yet effective cell vibration energy (CVE)
mapping method for image enhancement. Based on a hypothetical color-formation
mechanism, our proposed method first uses cell vibration and photoreceptor
correction to determine the photon flow energy for each color channel, and then
reconstructs the color image with the maximum energy constraint of the visual
system. Photoreceptor cells can adaptively adjust the feedback from the light
intensity of the perceived environment. Based on this understanding, we here
propose a new Gamma auto-adjustment method to modify Gamma values according to
individual images. Finally, a fusion method, combining CVE and Gamma
auto-adjustment (CVE-G), is proposed to reconstruct the color image under the
constraint of lightness. Experimental results show that the proposed algorithm
is superior to six state of the art methods in avoiding over-enhancement and
color distortion, restoring the textures of dark areas and reproducing natural
colors. The source code will be released at
https://github.com/leixiaozhou/CVE-G-Resource-Base.Comment: 13 pages, 17 figure
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