Article: Application of CNN image signal denoising technology in virtual reality environment Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.37 pp.18 - 40 Abstract: The virtual reality environment mainly comprises panoramic images, providing an immersive visual experience. However, factors like shooting environment, lighting conditions, and image compression often lead to noises, affecting the image quality and the user's immersive experience in the virtual environment. This paper implements a deep learning framework combining ResNet-50 and U-Net to effectively remove noise from panoramic images and improve the user's immersive experience. ResNet extracts deep features of images through a residual learning mechanism, enhances the precision of image alignment, and reduces the possibility of noise expansion. U-Net adopts an encoding-decoding structure, which preserves image details and denoises through skip connections, avoiding over-smoothing, to improve the denoising effect. The results show that at different noise intensities, the method in this paper is significantly better than the mean filtering and Frost-filter methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The PSNR value of the method in this paper is 36.89 dB when the noise intensity σ = 0.2, which is higher than that of the frost-filter (34.25 dB) and mean filtering (33.34 dB). Its SSIM values are above 0.90 under different noise intensities, which are higher than those of other methods. It can be found that the denoising model of U-Net + ResNet can comprehensively process various types of noise and show a better balance in restoring image details and structures, providing an effective solution for panoramic image denoising. Inderscience Publishers - linking academia, business and industry through research

Title: Application of CNN image signal denoising technology in virtual reality environment

Authors: Hairong Wang; Lingling Wang

Addresses: School of Network Engineering, Haikou University of Economics, Haikou, 571127, China ' School of Network Engineering, Haikou University of Economics, Haikou, 571127, China

Abstract: The virtual reality environment mainly comprises panoramic images, providing an immersive visual experience. However, factors like shooting environment, lighting conditions, and image compression often lead to noises, affecting the image quality and the user's immersive experience in the virtual environment. This paper implements a deep learning framework combining ResNet-50 and U-Net to effectively remove noise from panoramic images and improve the user's immersive experience. ResNet extracts deep features of images through a residual learning mechanism, enhances the precision of image alignment, and reduces the possibility of noise expansion. U-Net adopts an encoding-decoding structure, which preserves image details and denoises through skip connections, avoiding over-smoothing, to improve the denoising effect. The results show that at different noise intensities, the method in this paper is significantly better than the mean filtering and Frost-filter methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The PSNR value of the method in this paper is 36.89 dB when the noise intensity σ = 0.2, which is higher than that of the frost-filter (34.25 dB) and mean filtering (33.34 dB). Its SSIM values are above 0.90 under different noise intensities, which are higher than those of other methods. It can be found that the denoising model of U-Net + ResNet can comprehensively process various types of noise and show a better balance in restoring image details and structures, providing an effective solution for panoramic image denoising.

Keywords: image denoising; convolutional neural network; panoramic image; residual network; virtual reality.

DOI: 10.1504/IJICT.2025.149183

International Journal of Information and Communication Technology, 2025 Vol.26 No.37, pp.18 - 40

Received: 17 Jun 2025
Accepted: 19 Aug 2025
Published online: 16 Oct 2025 *

Title: Application of CNN image signal denoising technology in virtual reality environment

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