SCENet: Secondary Domain Intercorrelation Enhanced Network for Alleviating Compressed Poisson Noises

Abstract

In real image coding systems, block-based coding is often applied on images contaminated by camera sensor noises such as Poisson noises, which cause complicated types of noises called compressed Poisson noises. Although many restoration methods have recently been proposed for compressed images, they do not provide satisfactory performance on the challenging compressed Poisson noises. This is mainly due to (i) inaccurate modeling regarding the image degradation, (ii) the signal-dependent noise property, and (iii) the lack of analysis on intercorrelation distortion. In this paper, we focused on the challenging issues in practical image coding systems and propose a compressed Poisson noise reduction scheme based on a secondary domain intercorrelation enhanced network. Specifically, we introduced a compressed Poisson noise corruption model and combined the secondary domain intercorrelation prior with a deep neural network especially designed for signal-dependent compression noise reduction. Experimental results showed that the proposed network is superior to the existing state-of-the-art restoration alternatives on classical images, the LIVE1 dataset, and the SIDD dataset.

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“…In addition, P denotes a Poisson variable scaled by a with a mean value μ, and Q denotes a quantization table. Here, the probability distribution of the acquired value P(x) is derived in [4,5] as…”

“…It is considered that an accurate image degradation model and a robust image reconstruction technique for spatial-variant noise characteristics are additionally required for removing the compressed Poisson noise. Additionally, in [4], to observe the inter-block correlation distortion of an image owing to block-based compression, the original GT image is analyzed with various JPEG quality factors (q = 10, 20, and 30) corresponding to low transmission rates. While performing image compression at the compression level, the distribution of correlation values between the blocks was obtained, as shown in Figure 5.…”

“…Consequently, it can be determined that a correlation enhancement method for low-frequency DCT coefficients is necessary to effectively reconstruct a low-rate block-based compressed image. Based on the experiments presented in Figures 2-5, the compressed Poisson noise reduction technique in the secondary domain was proposed [4]. Here, the secondary block domain was proposed to enhance inter-block correlation, and the variance-stabilized neural network was proposed to cope with the spatial-variant noise characteristics.…”

“…Consequently, a method to more accurately reconstruct a high-resolution image by removing the compressed Poisson noises in the image is required. In this section, we introduce DNN-based denoising techniques [3,4] to remove the compressed Poisson noises and DNN-based super-resolution techniques to convert low-resolution images into high-resolution images.…”

“…In the same figure, the residual image obtained through the difference between the original ground truth (GT) image and the decoded image shows complex-pattern noise even in the flat region that had no signal. According to [4], the complex pattern deformed by compression is called compressed Poisson noise. It is considered that an accurate image degradation model and a robust image reconstruction technique for spatial-variant noise characteristics are additionally required for removing the compressed Poisson noise.…”