## 1 Introduction  
Hematoxylin and eosin (HE) stains are the principal tissue stains in histology and are the most common staining used in medical diagnosis. Eosin stains the cytoplasm and extracellular matrix red or pink, while hematoxylin stains the cell nuclei blue. Histopathological tissue slides contain a lot of important information for specialists to diagnose the patient or monitor the progress of the disease [1]. The invention of whole-slide imaging (WSI) scanners made digitizing entire slides with stained tissue possible, allowing the use of automatic image analysis techniques [2]. Numerous cell or tissue segmentation algorithms have been developed to analyze histopathological images [3; 4]. Thus, many steps of the WSI analysis workflow can be executed without pathological expertise to save pathologists time. The major challenge in the computerized analysis of digital pathology images is the size of WSIs, which might be bigger than 50,000 by 50,000 pixels. The most commonly used magnifications in WSI scanners are 20\(\times\) and 40\(\times\), with a spatial resolution of 0.50 \(\upmu\)m/pixel and 0.25 \(\upmu\)m/pixel, respectively. Digital slides are usually saved as SVS files. In this file format, images are stored as a tiled multi-resolution pyramid [5]. The high resolution of the images causes the file size to vary from several megabytes to tens of gigabytes [6]. Transmitting and storing these large files is challenging, so in most cases, the images are compressed using the JPEG or JPEG 2000 algorithms [7]. Joint Photographic Experts Group (JPEG) [8] compression first splits an image into \(8\times 8\) blocks and applies a discrete cosine transform (DCT) to each block. Then, these DCT coefficients are divided by a quantization table and rounded to the nearest integer to remove high-frequency details to save space. Thus, JPEG compression results in compression artifacts. The quantization table is typically represented by the integer value from 0 to 100, known as the Quality Factor (\(QF\)). Lower \(QF\) values mean a higher loss of information but a smaller file size. For example, when the quality factor \(QF=30\) is used, the resulting \(8\times 8\) blocks can be visually seen. These distortions can negatively impact the segmentation of small structures observed in the slide, like cells or nuclei. The topic of removing compression artifacts is covered widely in the literature. Depending on the project mechanism, compression artifacts removal methods can be divided into four main categories [9]: filter-based methods, probabilistic-prior-based methods, deep learning-based JPEG artifacts removal methods, and deep learning-based loop filter methods. Among filtering methods, we can distinguish adaptive filtering with locally adjusted filter kernels [10] and a trilateral deringing filter [11]. In [12], the authors proposed a shape-adaptive DCT filtering method, while in [13], the authors proposed an algorithm to preserve the details of the images and reduce the effect of quantization noise. They integrated QT decomposition with block-shift filtering. Some subsequent approaches are based on probability estimation of the image prior models. These include methods based on adaptive DCT transformation [12], Markov random field [14], or sparse coding [15; 16]. Deep learning has made breakthrough progress in removing JPEG compression artifacts in the past few years. The first work introducing deep models to the topic was the Artifacts Reduction Convolutional Neural Network [17], a model of a three-layer CNN, where an interpretable deep network was proposed to learn both the pixel-level regressive prior and the semantic-level discriminative prior. Some other examples are Trainable Nonlinear Reaction-Diffusion [18], and the Denoising Convolutional Neural Network [19].