Image super‐resolution based on the pairwise dictionary selected learning and improved bilateral regularisation

Image Super-Resolution Reconstruction Using Adaptive Co-sparse Regularization with Dual Dictionary

“…Chen [22], overcome the limitation of bicubic method by providing better algorithm which leads to be satisfactory SR reconstruction that contained a better visual quality but having less texture detailed compare with proposed method. Gou [21] achieved better results and outperforms previous method in terms of visual resolution but cannot maintain the smoothing edges of reconstructed SR image. In Fig.…”

“…Fig. 4(a) shows the original HR image, 4(b) shows the bicubic interpolation method, and 4(c)-(e) show the results of various algorithm Chen [22], Gou method [21], and the proposed method respectively. It can be seen from Fig.…”

“…Dong et al [20] has introduced the adaptively sparse regularization technique which utilizes a dictionary learning model which is constructed from a high resolution patches to generate an SR image, this approach inverses the artefacts and produce good resolution image with rich texture but it consumes large memory. New method has been applied recently to achieve the goal of an SR image by externally applied high resolution dictionary to reconstruct an image, the applied dictionary is not selected adaptively and processing of each patch required large amount of calculation, consequently leads to be large time consuming algorithm [21]. Chen et al [22] introduced an adaptive cosparse method to reconstruct a high resolution HR image, patch is divided under the subspace domain by cluster form and then learned the subdictionary from cluster respectively, and the resulting SR image is with less texture detailed due to missing HF component in applied dictionary.…”

“…In this study, we proposed a new method which uses co-sparse algorithm with paired dictionary to reconstruct an SR image, referring to the same idea [21]. The proposed method utilizes the prior input information which is get from dictionary of internal LR image, and external HR image respectively, to achieve the better resolution HR image.…”

“…1 shows the framework of the proposed method, which achieves the goal of SR image by utilizing the external dictionary. Inspired from the idea of [21], we have used two dictionaries to achieve the most part of the input LR image. Firstly, we construct an internal LR dictionary with an input LR image.…”