SSIM-based sparse image restoration

Omara, A. N.; Salem, Tarek M.; Elsanadily, Sherif; Elsherbini, M.M.

doi:10.1016/j.jksuci.2021.07.024

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…Table 3 shows the average results of the USC-SIPI dataset. As observed before in [30], iOMP doesn't exhibit whether significant or non-significant enhancements, in other words, all GSI levels of iOMP are below that of OMP. But, it is also noted that, iOMP gives a unique enhancement at k = 2.…”

Section: ) Average Results Of Usc-sipi Dataset (128 × 128 )supporting

confidence: 63%

“…But, it should be recalculated if this set is updated. In a later work [30], we discussed all issues of iOMP. In summary, our comments on iOMP are that, iOMP depends on the MSE-based OMP to select the suitable atoms from a dictionary.…”

Section: Ssim Inspired Omp Denoiser (Iomp)mentioning

confidence: 99%

“…In this section, to validate the performance of nOMP, another experiment is conducted on a huge number of images belonging to the USC-SIPI dataset (40 images) [38] and a Sentinel-1 dataset (40 images). In this experiment, iOMP and nOMP are compared to MiOMP [30]. Besides that, a comparison is conducted with another hybrid techniques, in which, a post enhancement stage is added to OMP and nOMP to make extra-enhancements.…”

Section: Experiments 3: Comparison To Miomp and Hybrid Techniquesmentioning

confidence: 99%

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nOMP: A New Sparse Solution to Enhance the SSIM Levels of OMP-Based Encoded Images

2022

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This work comprises the development of a quality enhancement technique for image encoders that use compressive sensing. The recommended solution seeks to maximize the perceptual quality based objective function, unlike other sparse representation algorithms that minimizes the error-based objective function. The key idea behind this work is to develop an iterative methodology that works as a modifier for the sparse coefficients. The modification procedure is SSIM-based and has been carried out in an iterative and linear manner. The conducted experiments revealed that the recommended technique works better than another SSIM-based modifier termed the SSIM-inspired OMP (iOMP) in terms of SSIM levels gained. The t-test is also utilized to examine our performance for significance, and the results show that the method works well for any type of image and any size, especially when a data-independent based dictionary is used.INDEX TERMS Orthogonal matching pursuit, compressive sensing, structural similarity index, image enhancement.

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Section: ) Average Results Of Usc-sipi Dataset (128 × 128 )supporting

confidence: 63%

Section: Ssim Inspired Omp Denoiser (Iomp)mentioning

confidence: 99%

Section: Experiments 3: Comparison To Miomp and Hybrid Techniquesmentioning

confidence: 99%

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nOMP: A New Sparse Solution to Enhance the SSIM Levels of OMP-Based Encoded Images

2022

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“…Clustering in RGB color space (RGB) can approximate fogless images with hundreds of colors. The pixels of each cluster are derived from the entire image, non-local regions, forming a NLP [17]. This theory is on the grounds of an improved NLP algorithm for underwater video image restoration and visual effect optimization, which can achieve effective restoration of underwater video images.…”

Section: A Implementation Of Underwater Video Image Restoration On Th...mentioning

confidence: 99%

Underwater Video Image Restoration and Visual Communication Optimization Based on Improved Non Local Prior Algorithm

Xia

2024

IJACSA

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Underwater image processing should balance image clarity restoration and comprehensive display of underwater scenes, requiring image fusion and stitching techniques. The pixel level fusion method is based on pixels, and by fusing different image data, it eliminates stitching gaps and sudden changes in lighting intensity, preserves detailed information, and thus improves the accuracy of stitching images. In the process of restoring underwater video images without local priors, there is still room for optimization in steps such as removing atmospheric light values, estimating transmittance, and calculating dehazing images through regularization. Based on the characteristics of Jerlov water types, water quality is classified according to the properties of suspended solids, and each channel is adjusted to the compensation space to improve the restoration algorithm. Background light estimation is used to determine the degree of image degradation, select the optimal attenuation coefficient ratio, and restore the image. The experimental results show that it is crucial to choose a ratio of attenuation coefficients that is close to the actual water quality environment being photographed. Both this model and traditional algorithms have an accuracy rate of over 99.0%, with the accuracy of this model sometimes reaching 99.9%. Pixel level fusion and background light estimation technology optimize underwater images, improve stitching accuracy and clarity, enhance target detection and recognition, and have important value for marine exploration rigs.

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Crafting transferable adversarial examples via contaminating the salient feature variance

Ren

Zhu

Sui

et al. 2023

Information Sciences

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SSIM-based sparse image restoration

Cited by 5 publications

References 37 publications

nOMP: A New Sparse Solution to Enhance the SSIM Levels of OMP-Based Encoded Images

nOMP: A New Sparse Solution to Enhance the SSIM Levels of OMP-Based Encoded Images

Underwater Video Image Restoration and Visual Communication Optimization Based on Improved Non Local Prior Algorithm

Crafting transferable adversarial examples via contaminating the salient feature variance

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