A new single image dehazing method with MSRCR algorithm

Wang, Jinbao; He, Ning; Lu, K.

doi:10.1145/2808492.2808511

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…Although MSR gives better results by combining dynamic range compression and colour rendition, it suffers from desaturation (greying-out) of uniform parts. One way to solve this problem is the use of color restoration function, which led to Multi-Scale Retinex with Color Restoration (MSRCR) development (Wang et al, 2015). The mathematical representation of MSRCR calculation formula can be written as:…”

Section: Related Workmentioning

confidence: 99%

Adaptive Illumination Correction Algorithm Based on Retinex Technology for Camera Trap Images

Зотин

Proskurin

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Camera traps generating a huge number of images help to study and monitor the wildlife. However, camera traps work at any time of the day and under any weather conditions. Therefore, many images have low or high illumination, blurring, and other defects. This complicates image analysis by both humans and computer systems. In this study, we develop an adaptive illumination correction algorithm based on a modified Multi-Scale Retinex (MSR). First, we accelerate computation by using recursive implementation of the Gaussian filter and utilizing look-up tables to find logarithms and new brightness values. Second, response of the MSR function is transformed by a modified threshold normalization to improve image quality. The upper and lower thresholds are calculated based on statistical information. Finally, we offer automatic adjustment of parameters depending on the area of the image in order to increase usability. Proposed algorithm was tested with various settings on a set of images obtained from camera traps. Experimental results show a high potential for its application.

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Section: Related Workmentioning

confidence: 99%

Adaptive Illumination Correction Algorithm Based on Retinex Technology for Camera Trap Images

Зотин

Proskurin

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Gibson and Nguyen [5] estimated the amount of fog in an image with a local Wiener filter. They used the theory of Retinex [10], [17] for single image dehazing. Multiscale Retinex with color restoration (MSRCR) can improve the visual rendering of an image even when the lighting conditions are not adequate.…”

Section: Related Workmentioning

confidence: 99%

Improved Stereo Image Dehazing Approach

Chang¹,

Way

Shih

2018

AIVP

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Numerous image dehazing algorithms have been studied intensively. However, most dehazing algorithms operate on single images. These algorithms produce inconsistent results if they are used to dehaze stereo images iteratively. In this paper, we present a novel dehazing approach for stereo images based on cross bilateral filtering. In this approach, we simultaneously estimate scene depth and dehaze the stereo images. The proposed approach is based on the observation of depth cues in the stereo images. Depth cues are mainly used to avoid inconsistent results, and the cross bilateral filter is used to preserve shape details. The results demonstrate that the proposed approach can deliver superior results to those of previously published methods.

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“…The algorithm based on this principle has been continuously improved. From the single-scale Retinex algorithm [9], to the multi-scale [10][11][12] weighted average MSR algorithm, and then to the color-recovery multi-scale MSRCR algorithm [13,14], some of the algorithm improvements have achieved breakthroughs in local and global dynamic compression, the enhancement of regional contrast, and the adjustment of color distortion. However, based on this theory, various methods still have problems such as a long processing time, limited model capacity, the inadaptability of hand-made constraints, the inability to process in batches quickly, glow appearing in shadow transitions in high-dynamic images and inferior color retention.…”

mentioning

confidence: 99%

Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

Guan,

Dong,

et al. 2024

Photonics

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The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes.

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A new single image dehazing method with MSRCR algorithm

Cited by 11 publications

References 11 publications

Adaptive Illumination Correction Algorithm Based on Retinex Technology for Camera Trap Images

Adaptive Illumination Correction Algorithm Based on Retinex Technology for Camera Trap Images

Improved Stereo Image Dehazing Approach

Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

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