Multiclass CNN-based adaptive optimized filter for removal of impulse noise from digital images

Roy, Amarjit; Sharma, Lakhan Dev; Shukla, Alok

doi:10.1007/s00371-022-02697-7

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…7, and the adaptive optimization filter based on multiclass CNN to remove impulse noise from digital images proposed in Ref. 43. The comparison results show that the denoising model in this paper retains more details in infrared images and is better applied in practice.…”

Section: Experiments and Results Analysismentioning

confidence: 90%

“…Figure 5 shows the denoising results of different denoising models for IR1-IR8, where the first column shows the denoising results of the proposed model for infrared images, and the remaining columns show the denoising results of the models in Refs. 16, 7, and 41–43 for infrared images, respectively. As shown in Fig.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…Tables 5–7 list the average PSNR, SSIM, and MAE values of the models in the Refs. 16, 7, and 41–43, and the proposed model after processing 100 images for eight IR scenes. Compared with other models, the performance indexes of the processed images of the proposed model are significantly improved, which again verifies the superiority of the proposed model.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

See 2 more Smart Citations

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

2023

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The traditional fractional order total variational model has better results in denoising and maintaining texture details in infrared images. However, it is difficult to determine the order of fractional order differentiation in image processing so that the model has the best denoising effect. To solve this problem, a fractional order total variational infrared image denoising model incorporating a flower pollination particle swarm optimization (PSO) algorithm is proposed in this paper. The model combines the search advantages of the flower pollination optimization algorithm and the PSO algorithm. The maximization multiobjective equation is designed as the fitness function of the optimization algorithm. The optimal order of the fractional order total variational model is found adaptively according to different features in different regions of the infrared image. The experimental results show that the improved model not only achieves the adaptivity of the adaption of the fractional order of total variational model order but also effectively removes the noise and retains the texture structure of infrared images to the maximum extent.

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Section: Experiments and Results Analysismentioning

confidence: 90%

Section: Experiments and Results Analysismentioning

confidence: 99%

Section: Experiments and Results Analysismentioning

confidence: 99%

See 1 more Smart Citation

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

2023

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“…A multiclass CNN 68 is employed to select an optimal window range. Subsequently, a vectored minimum mean value-based detection technique is applied to the pixel currently under operation within a specific image kernel to identify noise and choose the best window size around the pixel.…”

Section: Haze Removal Methodsmentioning

confidence: 99%

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

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Vision gets obscured in adverse weather conditions, such as heavy downpours, dense fog, haze, snowfall, etc., which increase the number of road accidents yearly. Modern methodologies are being developed at various academics and laboratories to enhance visibility in such adverse weather with the help of technologies. We review different dehazing techniques in many applications, such as outdoor surveillance, underwater navigation, intelligent transportation systems, object detection, etc. Dehazing is achieved in four primary steps: the capture of hazy images, estimation of atmospheric light with transmission map, image enhancement, and restoration. These four dehazing procedures allow for a step-by-step method for resolving the complicated haze removal issue. Furthermore, it also explores the limitations of existing deep learning-based methods with the available datasets and the challenges of the algorithms for enhancing visibility in adverse weather. Reviewed techniques reveal gaps in the case of remote sensing, satellite, and telescopic imaging. In the experimental analysis of various image dehazing approaches, one can learn the effectiveness of each phase in the image dehazing process and create more effective dehazing techniques.

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“…Recently, Convolutional Neural Networks (CNN) [1][2][3] and Generative Adversarial Network (GAN) -based methods [4][5][6] have achieved impressive performance for the IR task. Although these IR techniques can generate appropriate content for missing regions according to the remaining image patches, these methods still face significant challenges such as blurry artifacts in image restoration causing unpleasant visual effects.…”

Section: Introductionmentioning

confidence: 99%

SIDM-WIR: A Sampling Improved DiffusionModel for Worksite Image Restoration

Wen,

Zhuang,

Deng

2024

Preprint

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At presently, Diffusion Model (DM) has achieved state-of-the-art performanceby modeling the image synthesis process through a series of denoising networkapplications. Image Restoration (IR) is to improve the subjective image qual-ity corrupted by various kinds of degradation unlike image synthesis. However,IR for worksite images is greatly challenging in the low-level vision field due tocomplicated environmental factors. To solve this problem, we propose a Sam-pling Improved Diffusion Model for Worksite Image Restoration (SIDM-WIR).It not only improves the authenticity and representation ability for the gener-ation process, but also handles complex backgrounds and diverse object typeseffectively. SIDM-WIR has three main contributions: 1) Its framework adopts aMish-based residual module, which enhances the ability to learn complex patternsof images, and allows for the presence of negative gradients to reduce overfittingrisks during model training. 2) It employs a Mixed-head Self-Attention (MSA)mechanism, which augments the correlation among input elements at each timestep, and maintains a better balance between capturing the global structuralinformation and local detailed textures of the image. 3) It ran in a new customizeddataset named Workplace for WIR specifically. Furthermore, experiments on bothtwo datasets demonstrate that SIDM-WIR achieves better results to previousmethods both quantitatively and qualitatively.

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Multiclass CNN-based adaptive optimized filter for removal of impulse noise from digital images

Cited by 8 publications

References 34 publications

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

SIDM-WIR: A Sampling Improved DiffusionModel for Worksite Image Restoration

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