Hybridized Cuckoo Search with Multi-Verse Optimization-Based Patch Matching and Deep Learning Concept for Enhancing Video Inpainting

Rao, B. Janardhana; Chakrapani, Y.; Kumar, S. Srinivas

doi:10.1093/comjnl/bxab067

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Zhang et al [16] employed the combination of mean squared diffrerence and square of mean differences as a similarity metric to find the exemplar patch. The video inpainting using exemplar based methods are also evevated in recent years [17][18][19][20][21][22][23][24]. This study aimed to determine highest priority patch enhanced method to avoid dropping effect and new patch selection proces that yield favorable inpainting outcomes.…”

Section: Introductionmentioning

confidence: 99%

Development of Image Inpainting for object removal using Enhanced Patch Priority and Matching Measures

Rao,

Revathi,

Bhanusree

et al. 2024

E3S Web Conf.

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Image inpainting can be used to fix broken images and get rid of distracting elements. In exemplar based methods, patch priority computation and exemplar patch selection are crucial to the success of image inpainting technique. The dropping effect occurred in the highest patch priority computation and matching error in the best patch selection are the major issues in the exemplar inpaint methods. In this paper, the enhanced priority calculation technique is employed to avoid the dropping effect and introduced the new similarity measuring process, Mean Squared Error (MSD). The efficacy of the proposed techniques is estimated by comparing with the available methods in the literature qualitatively. It shows that proposed methods outperformed existing techniques.

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Section: Introductionmentioning

confidence: 99%

Development of Image Inpainting for object removal using Enhanced Patch Priority and Matching Measures

Rao,

Revathi,

Bhanusree

et al. 2024

E3S Web Conf.

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show abstract

“…This paper is based on the design of an effective deblurring method by integrating discriminative based denoisers with optimization model-based approaches. Convolutional Neural Networks (CNN) are used for learning the deblurring with Rectifier Linear Units (ReLU) [10], Adam [15], batch normalization [16] and dilated convolution [17] design networks with an efficient model. This model is further concatenated with the Iterative graph-based image restoration method.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN

Sree,

Nagajyothi,

Revathi

et al. 2023

IJRITCC

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Image degradation plays a major problem in many image processing applications. Due to blurring, the quality of an image is degraded and there will be a reduction in bandwidth. Blur in an image is due to variations in atmospheric turbulence, focal length, camera settings, etc. Various types of blurs include Gaussian blur, Motion blur, Out-of-focus blur. The effect of noise along with blur further corrupts the captured image. Many techniques have evolved to deblur the degraded image. The leading approach to solve various degraded images are either based on discriminative learning models or on optimization models. Each method has its own advantages and disadvantages. Learning by discriminative methods is faster but restricted to a specific task whereas optimization models handle flexibly but consume more time. Integrating optimization models suitably by learning with discriminative manner results in effective image restoration. In this paper, a set of effective and fast Convolutional Neural Networks (CNNs) are employed to deblur the Gaussian, motion and out-of-focus blurred images that integrate with optimization models to further avoid noise effects. The proposed methods work more efficiently for applications with low-level vision.

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An efficient VLSI architecture of 2‐D finite impulse response filter using enhanced approximate compressor circuits

Odugu

C²,

2021

Circuit Theory & Apps

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The very large‐scale integration (VLSI) design‐based architectures of 2‐D filters must have low power consumption and high speed rather than accuracy for image processing applications. The inexact computations reduce the delay, area, and power in arithmetic architectures, and utilizing this advantage an approximation can be introduced in 2‐D digital filters. In this work, circular symmetry is adopted to optimize the 2‐D finite impulse response (FIR) filter architecture, and it is implemented using imprecise multipliers and approximate compressors with acceptable accuracy. The exact 4:2 compressor is modified into an approximate 4:2 compressor by ignoring few inputs. Hence, hardware requirement and delay parameters are improved. The proposed compressor is designed by mixed logic rather than complete complementary metal‐oxide semiconductor (CMOS) logic. The dual value logic (DVL), transmission gate logic (TGL), and CMOS mixed logic gates are used to decrease the number of transistors in the circuits. The proposed compressors are used in the approximate Dadda multiplier (ADM). The ADMs are utilized to implement the 2‐D FIR filter architecture. The designed filter is coded in Verilog and implemented using field programmable gate array (FPGA). The hardware utilization is analyzed and compared with exact 2‐D FIR filters. Next, the proposed architecture is synthesized using Cadence genus tools, and physical design is carried out by Cadence Innovus tools in 45‐nm CMOS technology. The final physical design layout‐area, power, and delay reports are correlated with existing state‐of‐artworks. It is found that the proposed 2‐D FIR filter outperforms the existing works.

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Hybridized Cuckoo Search with Multi-Verse Optimization-Based Patch Matching and Deep Learning Concept for Enhancing Video Inpainting

Cited by 7 publications

References 31 publications

Development of Image Inpainting for object removal using Enhanced Patch Priority and Matching Measures

Development of Image Inpainting for object removal using Enhanced Patch Priority and Matching Measures

Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN

An efficient VLSI architecture of 2‐D finite impulse response filter using enhanced approximate compressor circuits

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