Learning-Based Joint Super-Resolution and Deblocking for a Highly Compressed Image

Li-Wei Kang, Member, IEEE, Chih-Chung Hsu, Student Member, IEEE, Boqi Zhuang, Chia-Wen Lin, Senior Member, IEEE, and Chia-Hung Yeh, Senior Member, IEEE

Department of Electrical Engineering
National Tsing Hua University
Hsinchu 30013, Taiwan





A highly compressed image is usually not only of low-resolution, but also suffers from compression artifacts (blocking artifact is treated as an example in this paper). Directly performing image super-resolution (SR) to a highly compressed image would also simultaneously magnify the blocking artifacts, resulting in unpleasing visual experience. In this paper, we propose a novel learning-based framework to achieve joint single-image SR and deblocking for a highly-compressed image. We argue that individually performing deblocking and SR (i.e., deblocking followed by SR, or SR followed by deblocking) on a highly compressed image usually cannot achieve a satisfactory visual quality. In our method, we propose to learn image sparse representations for modeling the relationship between low and high-resolution image patches in terms of the learned dictionaries for image patches with and without blocking artifacts, respectively. As a result, image SR and deblocking can be simultaneously achieved via sparse representation and MCA (morphological component analysis)-based image decomposition. Experimental results demonstrate the efficacy of the proposed algorithm.

Keywords: image super-resolution, sparse representation, dictionary learning, self-learning, image decomposition, morphological component analysis (MCA).

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Figure 1. Flowchart of the proposed self-learning-based super-resolution framework for a highly compressed image.

Figure 1 depicts the proposed framework for self-learning-based joint SR and deblocking for enhancing a downscaled and highly compressed image. Our method is to formulate the image enhancement problem as an MCA-based image decomposition problem via sparse representation. In our method, an input LR image I [see Fig. 1(a)] with blocking artifacts and its down-scaled version [see Fig. 1(b)] are first roughly decomposed into the corresponding low-frequency (LF) parts, and , and the high-frequency (HF) parts, and , respectively. Then, the respective most basic information will be retained in the LF parts [see Figs. 1(c) and 1(d)] while the blocking artifacts and the other edge/texture details will be included in the HF parts [see Figs. 1(e) and 1(f)] of the images. Then, we classify all of the patches in together with their corresponding patches in into two clusters of “blocking” and “non-blocking” HR/LR patch pairs. Based on the two training sets of patch pairs extracted from the input image itself, we learn two sets of coupled dictionaries, and , used for SR of blocking and non-blocking patches, respectively, as illustrated in Figs. 1(g) and 1(h), and Fig. 1(i).

To achieve the SR of , we perform patch-wise sparse reconstruction with the coupled dictionary set [see Fig. 1(i)] for each patch without blocking artifacts in . For each patch with blocking artifacts in , we perform SR reconstruction with , consisting of and , of corresponding HR/LR atoms, respectively, and MCA-based image decomposition to obtain the underlying HR patch. Then, can be simultaneously enlarged and decomposed into HR non-blocking and HR blocking components [see Figs. 1(j) and 1(k)]. We then add to the bicubic-interpolated [see Fig. 1(l)] to obtain the final SR result of I, as illustrated in Fig. 1(m). The detailed method will be elaborated below.



A. Image Results

Several LR JPEG images and YouTube videos with blocking artifacts were used to evaluate the performance of the proposed method. In our experiments, all of the test LR images were compressed by JPEG with quality factor (QF) ranging from 15 to 25. Different from [4], where the JPEG compression QF is required to be known in advance, our approach does not require any prior knowledge (including QF) about an input LR image. The parameter settings of the proposed method are described as follows. For each test LR image of size ranging from 140×140 to 442×400, the magnification factor, HR/LR patch sizes, the number of training iterations for dictionary learning, and the size  (number of atoms) of each learned dictionary (including , , , and ) are set to 2, 16×16/8×8, 100, and 1024, respectively. Our framework learns two pairs of dictionaries, respectively, for training patches with and without blocking artifacts, where each pair of dictionary includes 512 pairs of HR/LR atoms, resulting in the total dictionary size of 1024. In addition, the regularization parameter  used in the dictionary learning step in (9) is empirically set to 0.15, and the number of non-zero coefficients to be solved in each sparse reconstruction step in our method is set to 20.

Test image

Q factor: 15

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B. Video Results

To obtain the best quality of the demo videos, we suggest that the resolution of YouTube player could be adjusted as higher as it can. In these four videos, they are downloaded directly from YouTube with their naive resolution. In this case, there is no ground truth as reference.

Test Video I


Test Video II


Test Video III


Test Video IV



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