The degradation of image quality due to noise, blur, and low contrast
remains a significant challenge in various imaging applications,
particularly in medical diagnostics, remote sensing, and surveillance.
Effective restoration of such images is essential to enhance visual
clarity and extract meaningful information. Conventional techniques
often struggle to balance noise reduction and detail preservation. To
address these limitations, this study proposes an advanced multiframe
image restoration approach combining Contrast Limited Adaptive
Histogram Equalization (CLAHE) and Deep Belief Networks (DBN).
CLAHE is employed to enhance contrast adaptively, improving
visibility in regions with varying luminance. Subsequently, DBN, a
deep learning model, is applied to refine the reconstruction process by
leveraging its feature extraction and noise suppression capabilities.
This combination ensures that the restored images retain fine details
while effectively mitigating noise and distortions. Experimental
evaluation was conducted on a dataset of 500 degraded images,
including medical scans and natural scenes. The proposed method
achieved a Peak Signal-to-Noise Ratio (PSNR) of 36.2 dB, a Structural
Similarity Index (SSIM) of 0.92, and a contrast improvement rate of
48%, surpassing traditional methods like Bilateral Filtering and
Wavelet Transform. Processing time per image was maintained at an
efficient 1.8 seconds, ensuring practicality for real-time applications.
This novel integration of CLAHE and DBN shows significant
advancements in multiframe image restoration, making it a valuable
tool for applications requiring enhanced image quality. The approach
combines the strengths of contrast enhancement and deep learning-
based reconstruction, paving the way for improved image analysis and
decision-making in critical domains.
Allen Paul Esteban1, Prithviraj Singh Chouhan2, Aman Ahlawat3, Tarunika Dursinhbhai Chaudhari4 Nueva Ecija University of Science and Technology, Philippines1, Medicaps University, India2, Deenbandhu Chhotu Ram University of Science and Technology, India3, Government Engineering College, Dahod, India4
Multiframe Image Restoration, CLAHE, Deep Belief Networks, Image Quality Enhancement, Noise Reduction
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| Published By : ICTACT
Published In :
ICTACT Journal on Image and Video Processing ( Volume: 15 , Issue: 2 , Pages: 3433 - 3440 )
Date of Publication :
November 2024
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